Processing of continuous pressure-related signals derivable from a human or animal body or body cavity: methods, devices and systems

[0001] 1. Field of the Invention

[0002] Continuous monitoring of pressures from a human or animal body or body cavity requires some kind of processing of the pressure measurements. Pressure measurements within a human being or animal are created from pressure waves created by the cardiac beats, though this fact is not necessarily taken into account when measuring human or animal pressures. Pressure measurements may be derived from inside or outside a human or animal body or body cavity. It may be preferable to place a pressure sensor outside a body or body cavity, but the problem is to obtain reliable pressure measurements from such sensor locations. Depending on the type of such pressure measurements, signals related to pressure measurements may be garbled with noise, and the pressure difference from inside to outside the body cavity may be unknown. Pressure monitoring may have a more widespread role than reflected by the current and existing use. For example, various types of fluid flow valves are used to drain excess fluid from a body cavity such as a human brain or spinal fluid body cavity. Related to the function of such valve devices, pressure monitoring has no or a minimal place.

[0003] 2. Related Art

[0004] Continuous pressure monitoring has a widespread use concerning arterial blood pressure monitoring, ocular bulb pressure monitoring, intracranial pressure monitoring, lumbar cerebrospinal fluid pressure monitoring, urinary tract pressure monitoring, and gastrointestinal tract pressure monitoring. Depending on how pressure measurements are performed, continuous pressure signals may be obtained. Most current and existing technologies solely use analogue signals, though modern data technology allows such analogue signals to be converted into digital data signals. Some kind of signal processing may be applied to analogue as well as digital pressure signals.

[0005] Though monitoring of pressures within a human body or body cavity has been used for many decades, it is still unclear how pressure measurements should be processed to give best possible information from said measurements. It is well known that pressures have a static component (mean pressure) and a dynamic component (pulse pressure), related to the fact that pressure waves within a human or animal body or body cavity are created by cardiac beat-induced pressure waves. During pressure monitoring usually the static component is assessed whereas the role of the dynamic component is unclear. Technologies (e.g. by using fast Fourier transformation of pressure signals) that measure the dynamic component of pressure measurements give no or minimal control as to whether the dynamic pressure changes are related to cardiac beat-induced pressure waves or not.

[0006] Continuous pressure signals are processed by computation of mean pressure, usually computed as the sum of pressure levels divided by the numbers of samples. It is not possible to evaluate whether said mean pressure is related to cardiac beat-induced pressure waves or not. According to current and prior art technology, evaluation whether pressure measurements are associated with cardiac beat-induced pressure waves is based on visual inspection of the pressure wave, or by inspection whether diastolic and systolic pressure values are different. Such evaluation may be very user-dependent and misleading. For practical purposes, continuous inspection of a pressure waveform during pressure monitoring is impossible. Furthermore, waveform analysis according to prior art technology (e.g. fast Fourier analysis or modifications thereof), does not allow assessment whether pressure waves are related to cardiac beats or not. Given bad signal quality such types of analyses can be very misleading. The inventor previously has described a method for processing continuous pressure signals in the following patent application: U.S. Ser. Nos. 10/283,245; 10/613,112; PCT/NO02/00164; and PCT/NO 03/0029.

[0007] During pressure measurements it is preferable to use non-invasive sensors. The term non-invasive refers to the fact that the skin does not need to be penetrated to measure pressure within a body or body cavity. There are numerous examples of non-invasive pressure monitoring. Placing a device on the skin, thereby sensing the arterial blood pressure within the body tissue, may monitor arterial blood pressure. Transcranial Doppler may provide signals that are transformable into pressure-related signals indicative of intracranial pressure. Pressure-related signals indicative of intracranial pressure may as well be measured by means of a sensor device measuring air pressure within the outer ear channel after air-tight sealing of the outer ear by some kind of closing material to exclude interference from the atmospheric air pressure. The problem with so-called non-invasive pressure monitoring is that absolute pressure within the body or body cavity is unknown. The absolute pressure usually refers to the pressure difference between the pressures within the body or body cavity and the atmospheric pressure. Another major problem is that it can be impossible to know whether the pressure measurements are good or bad, i.e. whether the quality of the measurements or pressure signals are good or bad. This problem is at least partly related to the lack of a standard of what might be considered a good (or bad) pressure measurement.

[0008] Since the 1950's fluid flow valves have been used to drain excess fluid from a human brain or spinal fluid body cavity. Such fluid flow valves may be controllable, i.e. the degree of fluid drainage is adjustable. Such devices include some kind of mechanically adjustable valves. Pressure monitoring has received no or minimal role as related to said drainage of fluid from a brain or spinal fluid body cavity. A major cause is that pressure monitoring according to prior art technology measures absolute pressure, i.e. relative to atmospheric pressure. Changes in atmospheric pressure would change the zero pressure level and thus the measured pressure values. Sensor-related drift of zero pressure level also heavily affects the pressure measurements that are relative to atmospheric pressure.

[0009] Current and existing technology gives no or minimal opportunities for quality control whether a pressure signal incorporates pressure waves related to the cardiac beats or pressure waves related to artifacts or a combination of cardiac beat-induced and artifact-induced pressure waves. It is well known from prior art that each cardiac beat causes a pressure wave that is transferred from the intra-cardiac and intra-arterial compartments to the other human or animal body cavities. The shape and magnitude of the individual pressure waves depend on the particular body cavity, compartment of body cavity, the way of measuring the pressure wave, the absolute pressure level, and other factors such as posture and medications. Depending on how a pressure signal is measured, the pressure signal may contain noise of various types and pressure waves not related to cardiac beats. The challenge is to identify the single pressure waves corresponding to the cardiac beats, i.e. not to misinterpret an artifact-induced pressure wave as a cardiac beat-induced pressure wave. Said challenge is even greater when a pressure-related signal is derived from a non-invasive source containing much noise in the signal.

[0010] The first feature of this invention provides for a method for best possible differentiating between cardiac beat-induced pressure waves and pressure waves related to artifacts or a combination thereof. More specifically said first feature of the invention relates to: A method for processing continuous pressure-related signals derived from locations inside or outside a human or animal body or body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with a time reference, wherein for selectable time sequence windows the method comprises the further steps of:

• [0011] a) identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves,
• [0012] b) identifying from said digital data pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves,
• [0013] c) computing parameters selected from one or more of the list of:
• [0014] c1) single pressure wave (SW.x)-related parameters during individual of said time sequence windows,
• [0015] c2) delta single pressure wave (ΔSW.x)-related parameters between subsequent single pressure waves (n−1;n) within said time sequence windows,
  • [0016] said subsequent single pressure waves (n−1;n) representing a current single pressure wave SW[n].x in time n subtracted from the previous SW[n−1].x in time n−1 of said individual time sequence window,
• [0017] c3) time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows,
• [0018] c4) delta time sequence (ΔTS.x)-related parameters between subsequent time sequence windows (n−1;n) of said individual time sequence windows,
  • [0019] said subsequent time sequence windows (n−1;n) representing a current time sequence window TS[n].x in time n subtracted from the previous TS[n−1].x in time n−1 of said individual time sequence window,
• [0020] d) determining criteria for thresholds and ranges of parameters selected from one or more of the list of:
• [0021] d1) single pressure wave (SW.x)-related parameters of said single pressure waves during said time sequence windows,
• [0022] d2) delta single pressure wave (ΔSW.x)-related parameters between subsequent of said single pressure waves during said time sequence windows,
• [0023] d3) time sequence (TS.x)-related parameters of said single pressure waves during said time sequence windows,
• [0024] d4) delta time sequence (ΔTS.x)-related parameters between subsequent time sequence windows, and
• [0025] e) using said criteria for thresholds and ranges in step d) to provide optimal differentiating between single pressure waves caused by cardiac beat-induced pressure waves and pressure waves caused by artifact-induced pressure waves or a combination thereof.

[0026] This first feature of the invention should be considered an iterative process aiming at establishing a process method for optimal identification of pressure waves created by cardiac beat-induced pressure waves. Said criteria can be determined for specific signals in several recordings (each recording containing one or more signals). After determining said criteria in a selectable number of signals, these new criteria are determined and implemented in the inventive method. Both manual and automatic verification may be used to evaluate whether the improved method identifies pressure waves created by cardiac beat-induced pressure waves, not pressure waves created by artifacts or a combination thereof. Different criteria are determined for different types of signals, different locations of signals, and different sensors wherefrom signals are derived.

[0027] The significance of determining criteria related to thresholds and ranges of said single wave (SW.x)-, delta single wave (ΔSW.x)-, time sequence (TS.x)- and delta time sequence (ΔTS.x)-related parameters is that a precise tool is created for excluding pressure waves not related to cardiac beat-induced pressure waves. It has been shown that for specific types of signals and locations, said criteria are quite narrow for single pressure waves related to cardiac beat-induced pressure waves. This fact makes it easy to identify artifact-induced pressure waves, and it is very useful when comparing identical pressures derivable from different locations. Thus, a very precise tool for identification of pressure waves related to cardiac beat-induced pressure waves is established, which is crucial for other features of this invention. Such differentiation between single pressure waves created by cardiac beat-induced pressure waves, pressure waves caused by artifacts, or a combination of cardiac beat and artifact-induced pressure waves is not possible by currently used, prior art technology.

[0028] Current and prior art technology of processing pressure signals gives limited and less useful information about the pressures that are measured. An example is given. The major reason for continuous intracranial pressure (ICP) monitoring is to obtain information about cerebral elastance and compliance. Elastance (E) is a function of the relationship between pressure change and volume change (E=ΔP/ΔV). Increased cerebral elastance (E) implies that a small intracranial volume increase (ΔV) causes a large intracranial pressure increase (ΔP). Compliance (C) is the inverse of elastance (C=1/E). These relationships have been described in the pressure-volume curve of Langfit, which is well known from the prior art. Current, prior art technology of processing continuous intracranial pressure (ICP) signals gives no or minimal information about compliance. Instead rather complex pressure-volume relationships must be computed based on introduced volume changes during continuous intracranial pressure (ICP) monitoring. Another major challenge with current technology is that pressure measurements are very sensitive to calibration against a zero pressure level. Pressure signals derived from inside a human or animal body or body cavity usually are absolute pressure values, which are relative to atmospheric pressure level. Problems related to calibration against said atmospheric pressure level or related to drift of a zero pressure level during a measurement may give misleading pressure measurements.

[0029] In a second feature of this invention a method for processing continuous pressure-related signals is described that extracts new information about pressures from the pressure signal itself. More specifically said second feature of the invention relates to: A method for processing continuous pressure-related signals derived from locations inside or outside a human or animal body or body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with a time reference, wherein for selectable time sequence windows the method comprises the further steps of:

• [0030] a) identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves,
• [0031] b) identifying from said digital data pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves,
• [0032] c) computing time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows, and
• [0033] d) establishing an analysis output selected from one or more of said time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows:
  • [0034] d1) mean wave amplitude (TS.MeanWavedP),
  • [0035] d2) mean wave latency (TS.MeanWavedT),
  • [0036] d3) mean wave rise time coefficient (TS.MeanWaveRT),
  • [0037] d4) mean amplitude (TS.MeandP),
  • [0038] d5) mean latency (TS.MeandT),
  • [0039] d6) mean rise time coefficient (TS.MeanRT), and
  • [0040] d7) mean single wave pressure (TS.Mean_SWP).

[0041] The analysis output of said method may be presented in a variety of ways such as numerical values; trend plots of numerical values, histogram presentations or as a quantitative matrix. Thereby completely new information about pressures is obtained.

[0042] The significance of said analysis output is great. For example, computation of TS.MeanWavedP or TS.MeandP makes it possible to demonstrate reduced intracranial compliance, not revealed by current, prior art technology. Thus, information about intracranial compliance is derived from the intracranial pressure (ICP) signal itself. The determined values of these parameters are highly predictable of response to extra-cranial shunt treatment and selection of shunt valve opening. Determination of these parameters (e.g. TS.Mean_SWP) has made it possible to determine whether a pressure signal is of good quality or of bad quality. For example, in test measurements of several patients (both children and adults) it was found that intracranial pressure (ICP) values indicated normal pressures, despite subcutaneous (below skin) placement of a solid pressure sensor (Codman ICP Microsensor, Johnson & Johnson, Raynham, Mass.). Mean ICP was computed according to existing and prior art technology, as sum of pressure levels divided by number of samples independent whether pressure waves were related to cardiac beats or artifacts. In these cases current and prior art technology computed wrong and misleading pressure measurements. Determination of TS.Mean_SWP (or other of said TS.x-related parameters) according to this invention, revealed the bad signal quality since no single pressure waves were identified, and hence no time sequence (TS.x) parameters were computed. Furthermore, problems related to zero calibration against atmospheric pressure are eliminated by said second feature of this invention. This is related to the fact that TS parameters such as d1)-d7) all are relative values, not influenced by said atmospheric pressure level.

[0043] Current and existing technology includes a number of approaches and methods for non-invasive pressure monitoring, i.e. measuring pressures inside a body or body cavity without introducing the sensor inside said body or body cavity. For example, different variations of applanation technology may be used for measuring arterial blood pressure, ocular pressure (and intracranial pressure in infants). Pressure-related signals indicative of intracranial pressure (ICP) may be derived from transcranial Doppler signals, cranial impedance-related signals or pressure-related signals from within the outer ear channel after airtight sealing of the outer ear channel. However, a problem with said methods of non-invasive pressure monitoring is that only relative changes in pressure over time may be measured, related to the fact that the absolute pressures within the body cavity remain unknown during non-invasive pressure measurements. Another problem relates to the fact that current and existing technology of non-invasive pressure monitoring provides for no or minimal quality control whether the measured signals are good or bad. This is at least partly related to lack of a reference material of what is considered as good signals and bad signals.

[0044] A third feature of this invention provides for a method of comparing signals derived simultaneously from different locations. Thereby relationships between such simultaneous signals may be determined. A reference material can be built up for relationships between specific signals (and attributes such as location and sensor type). These established relationships may subsequently be used for formula-based adjustments of individual signals solely obtained by a non-invasive approach, thus modifying the individual continuous non-invasive pressure-related signals into signals highly predictable of the corresponding invasive pressure-related signals. More specifically said third feature of the invention relates to: A method for processing two or more simultaneous continuous pressure-related signals derived from a human or animal body from one or more locations thereof electable from: inside the body, outside the body, inside body cavity, outside body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with identical time reference, wherein for selectable and simultaneous time sequence windows the method comprises the further steps of:

• [0045] a) identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves within said two or more simultaneous signals constituting a pressure recording,
• [0046] b) identifying from said digital data pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves within said two or more simultaneous signals constituting a pressure recording,
• [0047] c) computing time sequence (TS.x)-related parameters of said single pressure waves during said identical time sequence windows within said two or more simultaneous signals constituting a pressure recording, wherein the method comprising the further steps of:
• [0048] d) determining relationships between time sequence (TS.x)-related parameters of said identical time sequence windows within said two or more simultaneous signals constituting a pressure recording,
  • [0049] said relationships calculated as related time sequence (rTS.x) parameters, and
• [0050] e) determining said related time sequence (rTS.x) parameters for individual recordings,
• [0051] f) determining said related time sequence (rTS.x) parameters for a population of recordings, and
• [0052] g) using said related time sequence (rTS.x) parameters for formula-based adjustment of time sequence windows of individual pressure-related signals,
• [0053] h) creating from said formula-based adjustments factorized time sequence (fTS.x) parameters of said individual time sequence windows of said individual continuous pressure-related signal.

[0054] Preferably said related time sequence (rTS.x) parameters should be obtained for a population of recordings in order to determine different formula-based relationships for specific types of signals and locations and sensor types. This procedure subsequently creates a reference material that may be applied on new and individual non-invasive pressure measurements. Thereby continuous pressure-related signals from a non-invasive source may be processed in a way that makes the continuous non-invasive pressure signals highly predictable of the pressures inside the body or body cavity.

[0055] One implementation (referred to as the second aspect) of said third feature of the invention is to provide for adjustment of individual non-invasive signals using formula-based related time sequence (rTS.x) parameters derived from a historical database, said historical database referred to as the first aspect of the third feature of the invention. The formula-based related time sequence (rTS.x) parameters may then be used to adjust e.g. pressure-related signals solely from a non-invasive source.

[0056] More specifically said second aspect of said third feature of the invention relates to: A method for processing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with a time reference, wherein for selectable time sequence windows the method comprises the further steps of:

• [0057] a) identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves,
• [0058] b) identifying from said digital data pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves,
• [0059] c) computing time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows,
• [0060] d) adjusting said time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows using formula-based adjustments based on related time sequence (rTS.x) parameters,
• [0061] e) creating from said formula-based adjustments factorized time sequence (fTS.x) parameters of said individual time sequence windows of said continuous pressure-related signals and
• [0062] f) establishing an analysis output selected from one or more of said factorized time sequence (fTS.x)-related parameters of said single pressure waves during individual of said time sequence windows.

[0063] The idea of said third feature of the invention is to provide for reliable non-invasive pressure monitoring. For example, pressure-related signals indicative of intracranial pressure can be obtained by placing a device sensing the air pressure within the outer ear channel after airtight sealing of said ear channel. However, the challenge is to provide reliable information about intracranial pressure from these non-invasive pressure-related signals. According to said first aspect of the invention, simultaneous contiuous pressure-related signals from a non-invasive and invasive sensor are compared to determine relationships between said signals. A purpose of the third feature is thus to merely create useful reference material to provide for adjustment (or calibration) of non-inasive signals obtained at another time. When only a non-invasive pressure monitoring is performed, this reference matrial can be used to adjust or calibrate the non-invasive signal, according to said second aspect of said third feature of the invention. Both said first and second aspects of said third feature of the invention depends on said first and second features; optimal differentiation between cardiac-beat and artifact-induced pressure waves is crucial for computing said time sequence (TS.x)-related parameters. The invention will thus provide useful aids for enabling optimum monitoring results when monitoring is performed at a later point of time.

[0064] It has been found that the significance of said third feature of the invention is great, as related to comparisons of identical pressures from different locations. By using the method described according to the third feature of the invention, continuous pressure signals derived from an epidural location became highly predictable of continuous pressure signals derived from within the brain parenchyma, i.e. intra-dural. Thereby, the invention enables epidural intracranial pressure (ICP) to be as precise as intra-dural pressure measurements from inside the brain parenchyma. This is a major advantage as epidural pressure measurements are less invasive, not requiring penetration of a cannula or sensor into the brain parenchyma. Furthermore, continuous pressure-related signals derived from the outer ear channel became highly predictable of continuous pressure signals derived from within the brain parenchyma itself. Thus, this third feature of the invention gives a technical solution to a major problem of non-invasive pressure recording, namely that absolute pressure relative to atmospheric pressure inside the body or body cavity is unknown during non-invasive pressure recording.

[0065] A technical problem related to measuring air pressure within the outer ear channel is to obtain airtight sealing of the outer ear channel. This may be practically difficult as the ear channel is very different from individual to individual.

[0066] According to a fourth feature of this invention, a technical solution is given to the problem of airtight sealing of an outer ear channel. A device for closing of said outer ear channel is described. More specifically said fourth feature of the invention relates to: A device for use in sensing continuous pressure-related signals through non-invasive pressure measurements on a human or animal body, comprising

• • [0067] a pressure sensor with a pressure sensing tube, said tube insertable into a human or animal outer ear channel spaced from a tympanic membrane thereof,
  • [0068] wherein inflatable means surrounds an outside length of the tube, said inflatable means upon inflation thereof sealingly closing an annular gap between a region of said tube and a wall region of said outer ear channel.

[0069] The significance of this device is that said inflatable means is thin-walled and soft, thus making airtight sealing of an outer ear channel possible, independent on the diameter of the outer ear channel. Thereby one size of said sensor may be used independent of the diameter of the outer ear channel.

[0070] Various parts of the methods related to said first, second and third features of the invention can be implemented in devices and system. Said devices and system are described in the fifth, sixth and seventh features of the invention.

[0071] According to an fifth feature of this invention a device is described for use in sensing continuous pressure-related signals. Said device can be used to display new information from pressure measurements derived from the inventive method of processing continuous pressure-related signals. More specifically said fifth feature of the invention relates to: A device for use in sensing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity, comprising

• • [0072] a pressure sensor with a pressure sensing element,
  • [0073] a pressure transducer capable of transforming said pressure-related signals into digital pressure-related signals,
  • [0074] a processing unit with input means for receiving said pressure-related digital signals, said processing unit providing at output means thereof one or more of the following time sequence parameters during selectable time sequence windows of said pressure-related signals:
    • [0075] mean wave amplitude (TS.MeanWavedP),
    • [0076] mean wave latency (TS.MeanWavedT),
    • [0077] mean wave rise time coefficient (TS.MeanWaveRT),
    • [0078] mean amplitude (TS.MeandP),
    • [0079] mean latency (TS.MeandT),
    • [0080] mean rise time coefficient (TS.MeanRT),
    • [0081] mean single wave pressure (TS.Mean_SWP),
  • [0082] a display unit connected to said output means for selectively displaying said one or more parameters, and
  • [0083] means for supplying power to power consuming parts of the device.

[0084] The significance of this fifth feature of the invention is important as compared to existent and prior art technology. The inventive method of processing continuous pressure-related signals providing for completely new information about pressures can be incorporated in this device. Various modifications of the device are within the scope of the invention. Thereby new information about pressures can be obtained in various settings such as within the home of a patient or within the hospital. Said new information can be derived from the continuous pressure signal itself and be displayed, enabling quick diagnosis and intervention.

[0085] According to the sixth feature of this invention a device is described for use in sensing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity. This device is a sensor device or a combined sensor-display device. The inventive method of processing continuous pressure-related signals is a part of said device. More specifically said sixth feature of the invention relates to: A device for use in sensing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity, comprising

• • [0086] a pressure sensor with a pressure sensing element,
  • [0087] a pressure transducer capable of transforming said pressure-related signals into digital pressure-related signals,
  • [0088] a processing unit with input means for receiving said pressure-related digital signals, said processing unit providing at output means thereof one or more of the following time sequence parameters during selectable time sequence windows of said pressure-related signals:
    • [0089] mean wave amplitude (TS.MeanWavedP),
    • [0090] mean wave latency (TS.MeanWavedT),
    • [0091] mean wave rise time coefficient (TS.MeanWaveRT),
    • [0092] mean amplitude (TS.MeandP),
    • [0093] mean latency (TS.MeandT),
    • [0094] mean rise time coefficient (TS.MeanRT),
    • [0095] mean single wave pressure (TS.Mean_SWP),
  • [0096] information transfer means connected to said output means and enabling transferal of information to an external unit of at least said one or more parameters, and
  • [0097] means for supplying power to power consuming parts within the device.

[0098] There are several major advantages with said sixth feature of the invention. By means of the sensor device itself completely new information from pressure measurements can be derived from a sensor device itself. Modifications of said sensor device are within the scope of the invention. One possible modification of said sensor device can be implanted within the patient. For example, said sensor device can be implanted within a human or animal body or body cavity, enabling pressure measurements at any time, using information transfer means of said sensor device. As compared to current and prior art technology, this sensor device provides completely new information about pressures. This new information is not dependent on a zero pressure level against atmospheric pressure, which is crucial for implantable modifications of said sensor device.

[0099] The seventh feature of this invention relates to a system for processing continuous pressure-related signals derivable from one or more sensor(s) having location(s) inside or outside a body or body cavity of a human being or animal. This system can be incorporated in various devices. The system incorporates the inventive method of processing continuous pressure-related signals. More specifically said seventh feature of the invention relates to: A system for processing continuous pressure-related signals derivable from one or more sensor(s) having location(s) inside or outside a body or body cavity of a human being or animal, said system comprising:

• [0100] means for on basis of said signals receivable from said sensor(s) via pressure transducer means to display output of said processing, and
• [0101] a processing unit in said system having means for processing said signals, said processing means including:
• [0102] a) sampling means for sampling said signals receivable from said pressure transducer means at specific intervals,
• [0103] b) converter means for converting the sampled signals received from said sampling means into pressure related digital data with a time reference,
• [0104] c) identifying means for during selectable time sequence windows identifying from said digital data output from said converter means single pressure waves related to cardiac beat-induced pressure waves, related to artifacts, or a combination of cardiac beat-induced waves and artifacts,
• [0105] d) computing means for computing time sequence parameters from included or selected time sequence windows output from said identifying means,
• [0106] e) analyzing means for analyzing said time sequence parameters in the form of digital data related to said selectable time sequence windows,
• [0107] f) output means for outputting to device terminal means one or more pressure parameters related to a selectable number of said time sequence windows:
  • [0108] f1) mean wave amplitude (TS.MeanWavedP),
  • [0109] f2) mean wave latency (TS.MeanWavedT),
  • [0110] f3) mean wave rise time coefficient (TS.MeanWaveRT),
  • [0111] f4) mean amplitude (TS.MeandP),
  • [0112] f5) mean latency (TS.MeandT),
  • [0113] f6) mean rise time coefficient (TS.MeanRT),
  • [0114] f7) mean single wave pressure (TS.Mean_SWP), and
• [0115] g) means for supplying power to power consuming parts within the system.

[0116] The important aspect of this seventh feature of the invention is that the inventive method can be incorporated within said system. Said system can further be incorporated in many devices, providing for new diagnostic information, not available by current and prior art technology.

[0117] The seven features of the invention described to this end related to methods, devices and system for processing continuous pressure-related signals. In the subsequent paragraphs another three features of the invention, disclosed as the eigth, ninth and tenth features, are described that relate to drainage of fluid from one body cavity to another. However, these latter three features of the invention are closely related to the methods, devices and system already summarized.

[0118] Drainage of fluid from a human brain or spinal fluid cavity can be required in the case of excess fluid within one of said cavities. Fluid flow valves developed for this kind of fluid drainage was introduced to the marked in the 1950's, and later on valves with adjustable fluid flow rate were introduced. Even after about half a century with using such valves for drainage of cerebrospinal fluid (CSF), there are still unsolved technical problems related to such valves: Over-drainage of cerebrospinal fluid (CSF) represents a great and unsolved problem with all kinds of shunts. No shunts allow for adjustment of shunt valve opening and fluid flow rate based on pressure measurements with determination of cerebral compliance.

[0119] According to a eighth feature of this invention, technical problems related to current and prior art technology of draining excess fluid are solved. More specifically said eighth feature of the invention relates to: A device for use in draining excess fluid from a brain or spinal fluid cavity unto another body cavity of a human being, comprising:

• • [0120] a first drainage tube having an inlet thereof located in said brain or spinal fluid cavity, said first drainage tube connected to the inlet of a fluid flow controllable valve,
  • [0121] a valve-opening regulator with associated control unit being connected to a regulator and processing unit, the control output from which is a function of pressure-sensing signals derived from at least one pressure sensor, a pressure transducer transforming said pressure-sensing signals into signals processed by said processing unit, a power supply, information transferable means, and
  • [0122] a second drainage tube from an outlet of said valve opening having a distal outlet thereof, said distal outlet opening into said another human body cavity, wherein
  • [0123] said first drainage tube, said fluid flow controllable valve, said valve-opening regulator, said control unit, said regulator, said processing unit, said pressure transducer, said power supply, said information transferable means and said second drainage tube being located below a skin surface of said human body.

[0124] The significance of the new inventive feature of this device relates to incorporation of said processing unit, which enables accurate control of valve opening and fluid flow rate. Said control is based on output of analyzing pressure-related signal from cavity wherein fluid is drained. Shunt valve opening and fluid flow rate may be regulated in a physiological way, i.e. the fluid flow rate is regulated according to intracranial compliance. Drainage of fluid is regulated to obtain optimal cerebral compliance. This inventive aspect represents a technical solution to the problem of over-drainage. In addition, in the case of suspected shunt failure, information about cerebral compliance can be obtained using said information transferable means.

[0125] According to a ninth feature of this invention a method is described for processing continuous pressure-related signals, said method being incorporated in said processing unit of said device for use in draining excess fluid from a brain or spinal fluid cavity. More specifically said ninth feature of the invention relates to: A method for processing continuous pressure-related signals derived from locations inside or outside a human body or body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with a time reference, wherein for selectable time sequence windows the method comprises the further steps of:

• [0126] a) identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves,
• [0127] b) identifying from said digital data pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves,
• [0128] c) computing time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows,
• [0129] d) establishing an analysis output of said time sequence (TS.x)-related parameters for a selectable number of said time sequence windows,
• [0130] e) establishing a deliverable first control signal related to an analysis output in step d) for a selectable number of said time sequence windows, said first control signal being determined according to one or more selectable criteria for said analysis output, and
• [0131] f) modifying said deliverable first control signal into a second control signal to provide a performance modifying signal.

[0132] The significance of this method is related to the fact that physiological regulation of shunt valve opening and valve fluid flow rate is found. It has been found that an analysis output related to TS.MeanWavedP (or other parameters such as e.g. TS.MeanWavedT, TS.MeandP, and TS.MeandT) was very useful for adjustment of shunt valve opening. The inventive method enables regulation of a shunt valve in a way that is not available by current, prior art technology. Furthermore, in case of suspected shunt dysfunction, computation of said TS.x parameters provides new information whether suspected shunt malfunction includes over- or under-drainage.

[0133] The identifying and computing steps of said process method related to the ninth feature of the invention are comparable to the identifying and computing steps of the process method related to the second feature of the invention.

[0134] Said methods of said first, second, third, and ninth features of this invention relate to methods for processing continuous pressure-related signals independent on locations of said continuous pressure-related signals. Examples of human or animal body cavities include: intracranial cavity (independent of whether intra- or extra-ventricular, intra-dural or epidural, spinal or cranial), ocular cavity, inner or outer ear cavities, intra-arterial cavity (independent of whether intra-arterial or intra-cardiac), intra-venous cavities (independent of wherein the various venous cavities), gastro-intestinal body cavities (independent of the specific type of gastrointestinal cavity such as esophageal pressure, duodenal pressure, intraperitoneal pressure), and urinary tract pressure (independent of which part of the urinary tract cavity). Said methods for processing continuous pressure-related signals are as well independent on type of pressure sensor, thus independent on whether said sensors are placed inside or outside a human or animal body or body cavity.

[0135] According to a tenth feature of this invention a system is described for processing continuous pressure-related signals, said system being used for controlling drainage of excess fluid from a first body cavity to a second body cavity of a human being. More specifically said tenth feature of the invention relates to: A system for processing continuous pressure-related signals derivable from one or more sensor(s) having location(s) inside or outside a body or body cavity of a human being, said system comprising:

• [0136] a) means for on basis of said signals receivable from said sensor(s) via pressure transducer means to control drainage fluid flow rate from a first body cavity to a second body cavity in one said human,
• [0137] b) a processing device in said system having means for processing said signals, said processing means including sampling means for sampling said signals at specific intervals,
• [0138] c) converter means for converting the sampled signals into pressure related digital data with a time reference,
• [0139] d) means for during selectable time sequence windows identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves, and related to artifacts or a combination of cardiac beat-induced waves and artifacts,
• [0140] e) means for computing and analyzing said digital data during said selectable time sequence windows,
• [0141] f) means for outputting to device terminal means one or more pressure parameter signals related to a selectable number of said time sequence windows:
  • [0142] f1) mean wave amplitude (TS.MeanWavedP),
  • [0143] f2) mean wave latency (TS.MeanWavedT),
  • [0144] f3) mean wave rise time coefficient (TS.MeanWaveRT),
  • [0145] f4) mean amplitude (TS.MeandP),
  • [0146] f5) mean latency (TS.MeandT),
  • [0147] f6) mean rise time coefficient (TS.MeanRT),
  • [0148] f7) mean single wave pressure (TS.Mean_SWP), and
• [0149] g) a valve device controlling the drainage fluid flow rate and connectable to said body cavities,
• [0150] h) regulator unit means connectable to said terminal means for receiving at least one of said parameter signals, said regulator unit means being capable of establishing a device performance modifying signal by means of one of said pressure parameter signals or a combination effect obtained from using at least two of said pressure parameter signals, wherein said performance modifying signal deliverable from said regulator unit being capable of controlling said drainage fluid flow rate through said valve device by input to a valve-opening regulator.

[0151] There are several major advantages with this tenth feature of the invention, as compare to current, prior art technology. The system enables assessment of cerebral compliance that may be used to control drainage of excess fluid, thereby ensuring optimal cerebral compliance. Furthermore, the system provides for quality control of the continuous pressure-related signals, thus reducing the risk of computing false or misleading pressure parameters.

[0152] The invention is now to be further described with reference to advantageous, exemplifying embodiments and alternatives thereof, as also supported by the attached drawings.

[0170] In FIG. 1 is shown an overview of notation and relationships related to continuous pressure signals, as used according to this invention. A recording 101 is one or more simultaneous signals 102 derivable from locations inside or outside a human or animal body or body cavity, each of said signals 102 having identical time reference, though it is not a requirement that start time 103 is identical for all signals of a recording. Each signal has the following attributes: type 104, frequency 105, and actual samples 106. Given these values, end time 107 can be calculated. According to this invention a specific sampling frequency 105, is not given. For single pressure wave analysis, it has been found that sampling frequencies of 100 to 200 Hz are useful. The notation List[x] is a reference to an element within an ordered list. With reference to FIG. 1, some examples are given:

[0171] The notation Recording [l].Signal [m].Time Sequence [o].Single Wave [q]) denotes a specific Single Wave [q] 108 within a specific Time Sequence [o] 109 within a specific Signal [n] 102, within a specific Recording [l] 101.

[0172] The notation Recording [l].Signal [m].Samples [n] denotes a specific Sample [n] 106 within a specific Signal [m] 102 within a specific Recording [l] 101.

[0173] Signal.Samples[n] denotes a specific sample within a non-specific signal, a general notation for a signal without specifying a dedicated signal.

[0174] The notation Recording [l].Signal [m].Delta Time Sequence [p] denotes a specific Delta Time Sequence [p] 110 within a specific Signal [m] 102 within a specific Recording [l] 101. The notation Recording [l].Signal [m].Time Sequence [o].Delta Single Wave [r] denotes a specific Delta Single Wave [r] 111 within a specific Time Sequence [o] 109 within a specific Signal [m] 102 within a specific Recording [l] 101.

[0175] The ordering is always starting with one, e.g. the first available time sequence window within the first available signal within a recording is denoted by Signal[1].Time Sequence[1].

[0176] Abbreviations may be used for Single Wave [m] (=SW[m]), Delta Single Wave [r] (=ΔSW[r]), Time Sequence [o] (=TS[o]) and Delta Time Sequence [p] (=ΔTS [p]).

[0177] It is important to note that attributes to type 104 are sensor 112 and location 113. Both these attributes are required to give a precise description of pressure signal type 104.

[0178] The notation Recording [l].Signal [m].Type.Location denotes a specific Location 113 within a specific Type 104 within a specific Signal [n] 102, within a specific Recording [l] 101. The notation Recording [l].Signal [m].Type.Sensor denotes a specific Sensor 112 within a specific Type 104 within a specific Signal [n] 102, within a specific Recording [l] 101. The present invention relates to a method of all types of human or animal continuous pressure-related signals 102.

[0179] The inventive method for processing continuous pressure-related signals 102 is independent on said locations 113, said locations being starting points (or origins) of said continuous pressure-related signals 102. Examples of said locations described in this document are given: Epidural refers to a location outside the dura mater of the brain but within the cranial vault. Intracranial/intra-dural refers to inside the dura mater of the brain. Spinal intra-dural refers to inside the dura mater of the spinal cord. Intra-arterial refers to a location inside an arterial blood vessel.

[0180] Said method for processing continuous pressure-related signals 102 is independent on type of pressure sensor 112, said sensor 112 being placed inside or outside a human or animal body or body cavity. There are many types of pressure sensors. A pressure sensor 112 can be a pressure measuring unit or pressure sensing element connected to a pressure conveying probe, e.g. a probe in the form of a fluid catheter and/or cannula. It should be understood that if a pressure sensor 112 is associated with a fluid cavity, e.g. in the brain, the pressure sensor 112 would conveniently be a element communicating with the cavity via the probe, unless the unit is so small in size that it can be safely located within the fluid cavity, e.g. at the proximal tip of a probe or catheter. A pressure sensor 112 can also be placed directly within the body or body cavity. Three examples of pressure sensors are listed: The Baxter (Truwave PX-600F vein/arterial anesthesia pressure monitoring kit) Health Care Corporation type of sensor measures fluid pressure within a catheter introduced via a cannula into a body cavity (e.g. cerebrospinal fluid body cavity, arterial blood cavity or venous blood cavity). Codman ICP MicroSensor™ (Johnson & Johnson, Raynham, Mass., USA) is a solid sensor which can be introduced into the brain parenchyma for intracranial pressure (ICP) monitoring. Spiegelberg ICP Probe 3XL (Spiegelberg, Aesculap, Germany) is a catheter which can be introduced into a brain cerebrospinal fluid (CSF) cavity wherein the pressure sensing element is outside the patient, thus measuring pressure within said Probe 3XL. These sensors are listed to exemplify that measurement of one pressure type (i.e. intracranial pressure) involves different sensor types, and different locations of the sensor elements (i.e. outside or inside the intra-dural space).

[0181] A pressure signal 102 refers to a number of sequential and variable pressure samples 106 during a time period. A signal 102 containing a continuous sequential number of samples 106 may be derived from a human or animal body from one or more locations thereof electable from: inside the body, outside the body, inside body cavity, outside body cavity.

[0182] As indicated in FIG. 1, a signal 102 is equivalent to: Recording [l].Signal [m].Samples [n].

[0183] A sample 106 is defined as: Pressure value at a specific time. Each of said samples contains a pressure value at a specific time. A selectable time sequence window is a selected time frame of said signal 102. Each of said selectable time sequence windows is related to a number of time-related sequential pressure samples 106, each sample 106 referenced by a sample number, and elapsed time that is determined by sample 106 location number and sample frequency 105. A specific duration of said time sequence windows is not given. However, for test purposes there has been used time sequence windows of 6 seconds duration. Said selected time frame lies in the range 5-15 seconds, though this represents no limitation of the scope of the invention.

[0184] A major problem with current and prior art technology of processing continuous pressure-related signals is a lack of useful methods for verifying whether pressure signals are based on pressure waves created by the cardiac beats, not resulting from artifact waves or a combination of artifact waves and cardiac beat-induced pressure waves. The first feature of this invention is a method for best possible differentiation between pressure waves caused by either cardiac beat-induced pressure waves, artifact-induced pressure waves or a combination of artifact- and cardiac beat-induced pressure waves. This first inventive feature is of major significance since pressure measurements based on artifacts are misleading, giving wrong diagnostic information. The significance is even greater when continuous pressure-related signals are derived from outside as compared to inside a human or animal body or body cavity due to a larger proportion of artifacts.

[0185] Reference is now given to the first feature of the invention, which is further detailed in independent claim 1 and subsequent sub-claims 2-58. Various aspects related to this first inventive feature are particularly illustrated in FIGS. 2, 3, 4, 5, 6, and 7.

[0186] First, reference is given to FIG. 2, providing an overview of the method for processing continuous pressure-related signals 201 derivable from locations inside or outside a human or animal body or body cavity, corresponding to said first feature of the invention. More details about the method are given together with detailed description of FIGS. 3, 4, 5, 6 and 7. The method for processing continuous pressure-related signals 201 derivable from locations inside or outside a human or animal body or body cavity, comprises the steps of sampling said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data signal 201 with a time reference. For selectable time sequence windows, the method comprises the further steps of identifying from said digital data the single pressure waves related to cardiac beat-induced pressure waves, and the pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves. The process of differentiating between these different types of pressure waves is illustrated in FIG. 2. The process method incorporates different Identifying Steps, Computing Step and Determining Steps.

[0187] Said Identifying Steps include identification 202 of all separate peaks and valleys in said sampled signal 201. Each of said peaks is a sample with a pressure value and a time stamp or location, and each of said valleys is a sample with a pressure value and a time stamp or location. The result of applying General Methods Criteria 203 is either included, i.e. accepted, peak/valley pair combinations 204 or excluded, i.e. rejected, peak/valley pair combinations 205. After applying the Single Wave & Delta Single Wave Criteria 206 to said included peak/valley pairs 204, the output is either included, i.e. accepted, single pressure waves 207 or excluded, i.e. rejected, pressure waves 208. Said criteria 206 relate to thresholds and ranges of single pressure wave (SW.x)-related parameters and delta single pressure wave (ΔSW.x)-related parameters during time sequence windows. Included pair combinations 207 of peak/valley 204 pairs in said signal 201 correspond to accepted pair combinations of diastolic minimum pressure (SW.P_min1) and systolic maximum pressure (SW.P_max), which characterize single pressure waves created by cardiac beat-induced pressure waves. Said criteria 206 exclude minimum-maximum pressure (SW.P_min1/SW.P_max) pairs with said single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters outside selectable thresholds and ranges. Pair combinations of diastolic minimum pressure (SW.P_min1) and systolic maximum pressure (SW.P_max) correspond to the diastolic minimum pressures and systolic maximum pressures of individual of pressure waves created by each of said cardiac beats. Time Sequence & Delta Time Sequence Criteria 209 are applied to each of said time sequence windows in a continuous series of said time sequence windows during a recording. Each time sequence window is a selected time frame of said signal. Said criteria 209 for thresholds and ranges of said time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters determine included, i.e. accepted, time sequence windows 210 and excluded, i.e. rejected, time sequence windows 211, that are used for further analysis. Said criteria 209 exclude time sequence windows 211 with said time sequence (TS.x)- and delta time sequence (ΔTS.x)-related parameters outside selectable thresholds and ranges.

[0188] At the Computing Step level, the following parameters are available: Single pressure wave (SW.x)-related parameters 212, delta single pressure wave (ΔSW.x)-related parameters 213, and time sequence (TS.x)-related parameters 214 for each individual of included time sequence windows 210. In addition, delta time sequence (ΔTS.x)-related parameters 215 are computed between subsequent time sequences (n−1; n) of said individual included time sequence windows 210. Said subsequent time sequence windows (n−1;n) represent a current time sequence window (TS[n].x) in time n subtracted from the previous TS[n−1].x in time n−1.

[0189] The Identifying Steps are applied to each of said time sequence windows in a continuous series of said time sequence windows during a recording.

[0190] The Computing Step is applied to each of said included time sequence windows 210 in a continuous series of said time sequence windows 210 during a recording.

[0191] In subsequent Determining Steps, criteria 216 are determined for thresholds and ranges of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters. One major application 217 of said criteria is to provide best possible differentiating between single pressure waves caused by cardiac beat-induced pressure waves and pressure waves caused by artifact-induced pressure waves or a combination thereof. By changing the set of criteria it is possible to change the proportion of excluded pressure waves and the proportion of excluded time sequence windows. There are several situations wherein the inventive step of determining criteria for optimal single pressure wave detection is crucial:

• • [0192] Determination of time sequence (TS.x)-related parameters derived from an invasive signal can be based on a minimal influence of artifact-induced pressure waves.
  • [0193] Determination of time sequence (TS.x)-related parameters derived from a non-invasive signal can be based on a minimal influence of artifact-induced pressure waves.
  • [0194] Determination of time sequence (TS.x)-related parameters derived from an invasive signal within a processing unit regulating a controllable shunt can be based on a minimal influence of artifact-induced pressure waves.

[0195] A more detailed description of General Methods Criteria (FIG. 2) is now given with reference to FIGS. 3a, 3b and 3c. The process described in this paragraph is intended to illustrate the concept, not to limit the scope of the invention. After the sampled pressure signal has been converted into a pressure-related digital data signal 301, all peaks 302 and valleys 303 in said sampled signal 301 are identified. Each of said peaks is a sample with a pressure value and a time stamp or location, and each of said valleys is a sample with a pressure value and a time stamp or location. Each individual peak 302 and valley 303 is identified with an absolute pressure value and a time stamp location.

[0196] First, it may be determined at which pressure levels the peaks 302 are located. The pressure level percentage lines 304 shown in FIG. 3a illustrate that 70.3% of all peaks within the 12 seconds recording period, have absolute pressure values between 20 and 35 mmHg, 22.2% of all peaks 302 have absolute pressure levels between 35 and 50 mmHg, and 7.5% of all peaks 302 have absolute pressure levels between 0 and 20 mmHg. Systolic maximum pressures (SW.P_max) 305, corresponding to cardiac beat-induced systolic maximum pressures probably should be at the absolute pressure level between 20 and 35 mmHg. A similar process is performed for all identified valleys 303 during this time period of 12 seconds. In the further analysis only those peaks 302 and valleys 303 with absolute pressure values within the majority pressure range are considered.

[0197] Another step is further illustrated in FIGS. 3b and 3c whereby nearby peaks 302 and valleys 303 are evaluated by using a so-called time window 306. A time window 306 refers to a selected time period, e.g. of 0.10 seconds duration. One of said General Methods Criteria (FIG. 2) defines that only the peak 302 with greatest absolute pressure value should be selected when several peaks 302 are identified within said time window 306.

[0198] Another of said General Methods Criteria (FIG. 2) define that only the valley 303 with the lowest absolute pressure value should be selected when several valleys 303 are identified within the same time window 306. In FIG. 3b is illustrated all the identified peaks 302 and valleys 303 within a short section of a continuous pressure-related signal 301. After application of the time window 306, it is shown in FIG. 3c that only peaks 302 with greatest absolute pressure values and valleys 303 with lowest absolute pressure values are remaining. In this example (FIG. 3c), the peak 302 corresponds to the systolic maximum pressures (SW.P_max) 305, and the valley 303 corresponds to the starting diastolic minimum pressure (SW.P_min1) 307. Thus, this process makes possible identification of included pair combinations of peaks 302 and valleys 303 in said signal 301. Other criteria determine that there cannot be two pair combinations of SW.P_max 305 and SW.P_min1 307 within one single wave duration (SW.WD), and that two different pair combinations of SW.P_max 305 and SW.P_min1 307 cannot contain either identical peaks 302 or valleys 303. It should be understood that various modifications, additions and improvements of said General Methods Criteria (FIG. 2) are within the scope of the invention.

[0199] The General Methods Criteria (FIG. 2) applied to continuous intracranial pressure (ICP) signals are shortly summarized:

• • [0200] Criteria_ICP_PeakValley_Y-WindowSize_MajorDistributionAreaPeaks:
    • [0201] a) Determine major distribution of peaks 302 to be used for further analysis.
  • [0202] Criteria_ICP_PeakValley_Y-WindowSize_MajorDistributionAreaValleys:
    • [0203] a) Determine major distribution of valleys 303 to be used for further analysis.
  • [0204] Criteria_ICP_PeakValley_X-WindowSize_GreatestPeakValue:
    • [0205] a) Determine the size of the window in which only one peak 302 with greatest absolute pressure value remains.
  • [0206] Criteria_ICP_PeakValley_X-WindowSize_LowestValleyValue:
    • [0207] a) Determine the size of the window in which only one valley 303 with lowest absolute pressure value remains.

[0208] It should be noted that the criteria listed here are shown to illustrate the use of said criteria, not to limit the scope of the invention. New and other criteria can be developed for specific types of signals.

[0209] As indicated in FIG. 3a, a continuous pressure-related signal 301 involves two dimensions, indicated by a time scale 308 and a pressure scale 309. It is also indicated that all peaks 302 and valleys 303 in said sampled signal 301 are identified during two consecutive time sequence windows, time sequence window no. one 310 and time sequence window no. two 311, each of 6 seconds duration. A time sequence window is a selected time frame of said sampled signal. The duration of said time sequence windows 310, 311 is selectable. For test purposes there were used durations of 6 seconds, though the exact duration of a time sequence represents no limitation of the invention. Preferably the duration of said selectable time sequence lies in the range 5-15 seconds. Each of said selectable time sequence windows is related to a number of time-related sequential pressure samples, each sample referenced by a sample number and elapsed time determined by sample location number and sample frequency. The method is applied to the continuous pressure-related signal for each of said time sequences in a continuous series of said time sequences during a continuous measurement period.

[0210] The notation Recording [l].Signals [m].Time sequence [o] denotes a specific Time sequence [o] within a specific Signal [m] within a specific Recording [l].

[0211] After determining included pair combinations of peaks 302 and valleys 303 in said signal 301, said Single Wave & Delta Single Wave Criteria (FIG. 2) are used to determine whether said peak 302/valley 303 pairs correspond to pair combinations of diastolic minimum pressure (SW.P_min1) 307 and systolic maximum pressure (SW.P_max) 305 that characterize single pressure waves created by the cardiac beat-induced pressure waves. All pair combinations of peaks 302 and valleys 303 correspond to potential pair combinations of SW.StartP_min1 307 and SW.P_max 305.

[0212] For included, i.e. accepted, single pressure waves 312 said peak 302/valley 303 pairs correspond to SW.P_max 305/SW.P_min1 307 pairs that also correspond to the diastolic minimum pressures and systolic maximum pressures of individual of pressure waves created by each of said cardiac beats. Thereby, each included single pressure wave 312 created by a cardiac beat-induced pressure wave is identified by systolic maximum pressure (SW.P_max) 305 related to a cardiac beat-induced systolic maximum pressure and a starting diastolic minimum pressure (SW.StartP_min1) 307 related to a cardiac beat-induced diastolic minimum pressure. The ending diastolic minimum pressure defines an end of a first single pressure wave (SW.P_min2) 313 which may be the same as the starting diastolic minimum pressure (SW.P_min1) defining the start of the subsequent second single pressure wave. If there is no subsequent second single pressure wave, the ending diastolic minimum pressure of a first single pressure wave (SW.P_min2) 313 is not same as the starting diastolic minimum pressure (SW.P_min1) 307 of another single pressure wave.

[0213] Excluded, i.e. rejected, pressure waves 314 contain peak 302/valley 303 pairs that do not meet the Single Wave & Delta Single Wave Criteria (FIG. 2). Said excluded pressure waves 314 are created by artifacts or a combination of cardiac beat- and artifact-induced pressure waves.

[0214] The Single Wave & Delta Single Wave Criteria (FIG. 2) applied to the continuous intracranial pressure (ICP) signal is shortly summarized:

• • [0215] Criteria ICP Intra-dural SW.dP:
    • [0216] a) Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for pair combinations of SW.P_min1 307 and SW.P_max 305 to be included for further analysis.
  • [0217] Criteria ICP Intra-dural SW.dT:
    • [0218] a) Latency (SW.dT) must be between 0.10 to 0.40 seconds for pair combinations of SW.P_min1 307 and SW.P_max 305 to be included for further analysis.

[0219] The specific criteria used in this example are not intended to limit the scope of the invention, but to illustrate application of said criteria. New and other criteria can be developed for specific types of signals, and criteria are determined for all single wave (SW.x)-related parameters and delta single pressure wave (ΔSW.x)-related parameters.

[0220] The Identifying Steps (FIG. 2) determining included single pressure waves are further illustrated in FIGS. 4a and 4b. In FIG. 4a is shown the step of identifying all separate peaks and valleys, and in FIG. 4b is shown the step of determining included single pressure waves on the basis of included peak/valley pairs. In FIG. 4a is shown a continuous intracranial pressure (ICP) signal 401 including pressure waves 402. The two dimensions of the pressure signal are indicated, namely the pressure scale 403 and the time scale 404. Only one time sequence window 405 of the continuous pressure signal 401 is shown (Recording[46].Signal[1]TimeSequence[360]), including all twenty-two identified peaks 406 (filled squares) and all seventeen identified valleys 407 (open circles). Each of said peaks 406 is a sample with a pressure value and a time stamp or location, and each of said valleys 407 is a sample with a pressure value and a time stamp or location.

[0221] Further application of the General Methods Criteria (FIG. 2) determine included pair combinations of peaks 406 and valleys 407 in said signal 401.

[0222] Application of the Single Wave & Delta Single Wave Criteria (FIG. 2) to these included peak 406/valley 407 pairs determine included single pressure waves 408 and excluded single pressure waves 409 within said time sequence window 405 (Recording[46].Signal[1]TimeSequence[360]).

[0223] Only two sets of Single Wave & Delta Single Wave Criteria were applied to the peak 406/valley 407 pairs shown in FIG. 4a:

[0224] Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for peak 406/valley 407 pairs to be included for further analysis (=Criteria_ICP_Intra-dural_SW.dP); and latency (SW.dT) must be between 0.10 to 0.40 seconds for peak 406/valley 407 pairs to be included for further analysis (=Criteria_ICP_Intra-dural_SW.dT).

[0225] Included peak 406/valley 407 pairs correspond to included, i.e. accepted, pair combinations of systolic maximum pressure (SW.P_max) 410 and starting diastolic minimum pressure (SW.P_min1) 411, characterizing included single pressure waves 408 that are created by the cardiac beat-induced pressure waves. Said included single pressure waves 408 (identified by included SW.P_max 410/SW.P_min1 411 pairs) are shown in FIG. 4b (termed SW[1], SW[2], SW[3], SW[4], and SW[5]). The ending diastolic minimum pressure (SW.P_min2) 412 defining an end of single pressure wave three (SW[3]) is notably not the same as starting diastolic minimum pressure (SW.P_min1) 411 defining the start of single pressure wave four (SW[4]). On the other hand, the ending diastolic minimum pressure (SW.P_min2) 412 defining an end of single pressure wave four (SW[4]) is the same as the beginning diastolic minimum pressure (SW.P_min1) 411 defining the start of single pressure wave five (SW[5]). Said excluded pressure waves 409 (identified as excluded, i.e. rejected, peak 406/valley 407 pairs) are also indicated in FIG. 4b, shown as the two pressure waves between single pressure wave three (SW[3]) and four (SW[4]). A visual inspection of the pressure waves 402 shown in FIGS. 4a and 4b suggest that also the two excluded pressure waves 409 might have been included, given another set of Single Wave & Delta Single Wave Criteria. Thus, the output of the Identifying Steps (FIG. 2) applied to the peaks 406 and valleys 407 shown in FIGS. 4a and further shown in FIG. 4b illustrate the fact that the General Methods Criteria and Single Wave & Delta Single Wave Criteria heavily influence the proportion of included single pressure waves 408 and excluded pressure waves 409.

[0226] The time sequence window 405 (TimeSequence[360]) shown in FIGS. 4a and 4b is one individual of said time sequence windows 405 in a continuous series of time sequence windows 405 (TimeSequence[1] to TimeSequence[4665]) during this particular recording (Recording[46]): Recording[46]. Signal [1].TimeSequence[360].

[0227] The method is applied to each of said time sequence windows 405. To further evaluate the included single pressure waves 408 within said time sequence window 405, said Time Sequence & Delta Time Sequence Criteria (FIG. 2) are applied. Only one criterion was applied to the time sequence window 405 shown in FIGS. 4a and 4b (TimeSequence[360]), namely that TS.SWCount must be between 4 and 18 counts (=Criteria_ICP_TS.SWCount_SWCountRange). The output of applying said criteria are either included, i.e. accepted, or excluded, i.e. rejected, time sequence windows (see FIG. 2), wherein the time sequence window shown in FIG. 4b is an included time sequence window.

[0228] A subgroup of selected Time Sequence and Delta Time Sequence Criteria define whether single pressure waves 408 which occur between two consecutive of said time sequence windows 405 are to be placed within one or the other of said two consecutive individual time sequence windows. Said selected criteria define that a first of said single pressure waves (e.g. SW[1]; FIG. 4b) 408 within said individual time sequence window 405 must have its ending diastolic minimum pressure value (SW.P_min2) 412 within said individual time sequence window 405. Said selected criteria also define that a last of said single pressure waves (e.g. SW[5]; FIG. 4b) 408 within said individual time sequence window 405 must have both its starting diastolic minimum pressure value (SW.P_min1) 411 and its ending diastolic minimum pressure value (SW.P_min2) 412 within said individual time sequence window 405. On this basis, the SW.P_min1 411/SW.P_max 410 pair combination subsequent to single pressure wave five (SW[5]) is not included (is disregarded) within this particular time sequence window 405 (TimeSequence[360]). The starting diastolic minimum pressure value (SW.P_min1) 411 of this SW.P_min1 411/SW.P_max 410 pair is the same as the ending diastolic minimum pressure value (SW.P_min1) 412 of single pressure wave five (SW[5]). This SW.P_min1 411/SW.P_max 410 pair was not included within this time sequence window 405 due to no identified ending diastolic minimum pressure value (SW.P_min2) 412 within said individual time sequence window 405.

[0229] An important issue is how to verify that included single pressure waves 408 determined as SW.P_min1 411/SW.P_max 410 pair combinations correspond to cardiac beat-induced pressure waves. The verification process may either be manual or automatic. During manual verification, visual inspection of valley 407/peak 406 pair detections (or SW.P_min1 411/SW.P_max 410 pair detections) is performed for individual signals 401 of individual recordings. It is visually inspected how changes in sets of criteria (i.e. General Methods Criteria, Single Wave & Delta Single Wave Criteria, and/or Time Sequence & Delta Time Sequence Criteria) modify said detections of valley 407/peak 406 pairs and/or SW.P_min1 411/SW.P_max 410 pairs. During automatic verification, detections of valley 407/peak 406 pairs and/or SW.P_min1 411/SW.P_max 410 pairs may be compared against a reference material. Such a reference material may be another signal within an identical recording, wherein samples are obtained from each respective one of said pressure related signals, each such sample containing a pressure value at a specific time, and wherein said two or more pressure related signals are all sampled simultaneously.

[0230] Examples of such simultaneous pressure-related signals are continuous invasive intracranial pressure (ICP) and arterial blood pressure (ABP) signals. Instead of a continuous pressure related signal a continuous electrocardiogram (ECG) signal may as well be selected. A continuous electrocardiogram (ECG) signal gives a very precise heartbeat corresponding to the cardiac beat-induced heartbeats. Thus, during automatic verification of detections of location of starting diastolic minimum pressure values (SW.P_min1) 412, comparisons are made against location of diastolic minimum pressure derived from electrocardiogram (ECG) signals. It should be noted, however, that there could be a delay in time (milliseconds) between sampled ECG and ICP signals caused by natural technical causes. The ICP's observed SW.P_min1.Locations will natural be within a constant delay compared to the ECG's observed SW.P_min1.Locations. Nevertheless, the heartbeat duration should be identical. Another method for automatic verification is made by recalculating all available manually verified recordings whenever criteria are changed or methodological improvements are made. Such a recalculation can be automatically compared against the reference result.

[0231] During the Identification Steps the output is included time sequence windows after application of the Time Sequence & Delta Time Sequence Criteria (see FIG. 2). Based on said included time sequence windows, a number of parameters: single wave (SW.x)-related parameters, delta single pressure wave (ΔSW.x)-related parameters, time sequence (TS.x)-related parameters, and delta time sequence (ΔTS.x)-related parameters are obtainable. In the following paragraphs reference is given to these parameters.

[0232] Reference is now given to FIGS. 5a focusing on single wave (SW.x)-related parameters. Within a continuous pressure-related signal 501, one individual of said included single pressure waves 502 is shown: Recording[46].Signal[1].TimeSequence[360].SingleWave[4]. This single pressure wave is created by one individual cardiac beat. First, it should be noted that a continuous pressure-related signal 501 involves two dimensions, namely a pressure scale 503 and a time scale 504. Both dimensions have to be considered when describing a continuous pressure-related signal 501. Therefore, single pressure wave (SW.x)-related parameters are with reference to time location and pressure level value. For example, the parameter starting diastolic minimum pressure (SW.P_min1) 505 involves the two values SW.P_min1.Location (referring to time stamp location) and SW.P_min1.Value (referring to pressure level value). This aspect must be remembered for the various single pressure wave (SW.x)-related parameters, though reference is not specifically given to time location and pressure level value when referring to these parameters.

[0233] The single pressure wave (SW.x)-related parameters computed during said selected time sequence windows are selected from the group of:

• • [0234] starting diastolic minimum pressure defining the start of the single pressure wave (SW.P_min1) 505, as further detailed in equation (1):
    SW[n].P_min1.Value=Signal.Samples[SW[n].P_min1.Location]  (1)
  • [0235] ending diastolic minimum pressure defining the end of the single pressure wave (SW.P_min2) 506, as further detailed in equation (2):
    SW[n].P_min2.Value=Signal.Samples[SW[n].P_min2.Location]  (2)
  • [0236] single wave sample count defining the number of samples within a single pressure wave, as further detailed in equation (3):
    SW[n].SampleCount=SW[n].P_min2.Location−SW[n].P_min1.Location   (3)
  • [0237] systolic maximum pressure of the single pressure wave (SW.P_max.) 507, as further detailed in equation (4):
    SW[n].P_max.Value=Signal.Samples[SW[n].P_max.Location]  (4)
  • [0238] amplitude of the single pressure wave (SW.dP) 508, as further detailed in equation (5):
    SW[n].dP=SW[n].P_max.Value−SW[n].P_min1.Value  (5)
  • [0239] latency of the single pressure wave (SW.dT) 509, as further detailed in equation (6):
    SW[n].dT=(SW[n].P_max.Location−SW[n].P_min1.Location)/Signal.Frequency  (6)
  • [0240] rise time coefficient of the single pressure wave (SW.RT), as further detailed in equation (7):
    SW[n].RT=SW[n].dP/SW[n].dT  (7)
  • [0241] wave duration of the single pressure wave (SW.WD) 510, as further detailed in equation (8):
    SW[n].WD=SW[n].SampleCount/Signal.Frequency  (8)
  • [0242] mean single wave pressure of the single pressure wave (SW.Mean_SWP), as further detailed in equation (9):

[0243] $\begin{array}{cc}\mathrm{SW}\left[n\right].{\mathrm{Mean}}_{\mathrm{SW}}P=\frac{\sum _{\mathrm{loc}=\mathrm{SW}\left[n\right].P_{\mathrm{min1}}}^{\mathrm{SW}\left[n\right].P_{\mathrm{min2}}-1}\left(\mathrm{Signal}.\mathrm{Samples}\left[\mathrm{loc}\right]\right)}{\mathrm{SW}\left[n\right].\mathrm{SampleCount}}& \left(9\right)\end{array}$

• • [0244] diastolic minimum pressure difference of the single pressure wave (SW.Diff_P_min) 511, as further detailed in equation (10):
    SW[n].Diff_—P_min=SW[n].P_min2,Value−SW[n].P_min1.Value  (10)

[0245] The ending diastolic minimum pressure (SW.P_min2) 506 defines an end of a single pressure wave 502, and the starting diastolic minimum pressure (SW.P_min1) 505 defines the start of a single pressure wave 502. Said single pressure wave amplitude (=SW.dP) 508 equals systolic maximum pressure value (SW.P_max.Value) 507 minus starting diastolic minimum pressure value (SW.P_min1.Value) 506. Single pressure wave latency (=SW.dT) 509 equals time duration starting diastolic minimum pressure (SW.P_min1.Location) 505 to systolic maximum pressure (SW.P_max.Location) 507. Single pressure rise time coefficient relates to the relationship between amplitude (SW.dP) 508 and latency (SW.dT) 509 (SW.RT=SW.dP/SW.dT). Wave duration (SW.WD) 510 for each individual of said single pressure waves 502 relates to the time duration between starting diastolic minimum pressure (SW.P_min1) 505 and ending diastolic minimum pressure (SW.P_min2) 506. Diastolic minimum pressure difference (SW.Diff_P_min) relates to pressure difference between starting and ending diastolic minimum values (SW.P_min1 505 versus SW.P_min2 506) of one individual of said single pressure waves 502.

[0246] Mean single wave pressure (SW.Mean_SWP) for each individual of said single pressure waves 502 relates to absolute mean pressure during the time of the wave duration, i.e. from starting diastolic minimum pressure (SW.P_min1) 505 to ending diastolic minimum pressure (SW.P_min2) 506. Mean pressure for an individual single pressure wave (SW.Mean_SWP) is the sum of sample values within said pressure wave divided by numbers of samples.

[0247] Except for mean single wave pressure (SW.Mean_SWP), all single wave (SW.x)-related parameters are relative values in either pressure or time. The relative pressure levels are crucial in the way that these relative values are independent on zero pressure level or drift in zero pressure level.

[0248] In FIG. 5b the first single pressure wave 502 is termed Single Wave[l] (n−1) and the subsequent and second single pressure wave 502 is termed Single Wave[2] (n). As indicated (FIG. 5b), the ending diastolic minimum pressure (SW.P_min2) 506 of Single Wave[1] also is starting diastolic minimum pressure (SW.P_min1) 505 of Single Wave[2]. In another situation, the ending diastolic minimum pressure (SW.P_min2) 506 of a first single pressure wave is not the same as starting diastolic minimum pressure (SW.P_min1) 505 of a subsequent second single pressure wave, provided that this second single pressure wave is not following immediately after the first one.

[0249] The delta single pressure wave (ΔSW.x)-related parameters during said selected time sequence windows are illustrated in FIG. 5b, wherein the parameters are selected from the group of:

• • [0250] systolic maximum pressure difference between two subsequent (n−1;n) single pressure waves (ΔSW.Diff_P_max) 512, as further detailed in equation (11):
    ΔSW[n].Diff_—P_max=SW[n].P_max.Value−SW[n−1].P_max.Value  (11)
  • [0251] amplitude difference between two subsequent single pressure waves (ΔSW.Diff_dP), as further detailed in equation (12):
    ΔSW[n].Diff_—dP=SW[n].dP−SW[n−1].dP  (12)
  • [0252] latency difference between two subsequent single pressure waves (ΔSW.Diff_dT), as further detailed in equation (13):
    ΔSW[n].Diff_—dT=SW[n].dT−SW[n−1].DT  (13)
  • [0253] rise time coefficient difference between two subsequent single pressure waves (ΔSW.Diff_RT), as further detailed in equation (14):
    ΔSW[n].Diff_—RT=SW[n].RT−SW[n−1].RT  (14)
  • [0254] wave duration difference between two subsequent single pressure waves (ΔSW.Diff_WD), as further detailed in equation (15):
    ΔSW[n].Diff_—WD=SW[n].WD−SW[n−1].WD  (15)
  • [0255] mean single wave pressure difference between two subsequent single pressure waves (ΔSW.Diff_Mean_SWP), as further detailed in equation (16):
    ΔSW[n].Diff_Mean_SWP=SW[n].Mean_SWP−SW[n−1].Mean_SWP  (16)

[0256] Two subsequent single pressure waves (n−1;n) represent a current single pressure wave SW[n].x in time n subtracted from the previous SW[n−1].x in time n−1 of said individual time sequence window.

[0257] As indicated in FIG. 5b, systolic maximum pressure difference (SW.Diff_P_max) 512 relates to pressure difference between systolic maximum pressure (SW.P_max) 507 values of two subsequent of said single pressure waves (SW[2].P_max−SW[1].P_max; FIG. 5b). Amplitude difference (SW.Diff_dP) relates to difference in single pressure wave amplitudes (SW.dP) 508 of two subsequent of said single pressure waves (SW[2].dP−SW[1].dP; FIG. 5b). Latency difference (SW.Diff dT) relates to difference in single pressure wave latency (SW.dT) 509 of two subsequent of said single pressure waves (SW[2].dT−SW[1].dT; FIG. 5b). Rise time coefficient difference (SW.Diff_RT) relates to difference in single pressure wave rise time coefficient (SW.RT) of two subsequent of said single pressure waves (SW[2].RT−SW[1].RT; FIG. 5b). Wave duration difference (SW.Diff_WD) relates to difference of wave duration (SW.WD) 510 between two subsequent of said single pressure waves (SW[2].WD−SW[1].WD; FIG. 5b). Mean single wave pressure difference (SW.Diff_Mean_SWP) relates to difference of mean single wave pressure (Mean_SWP) between two subsequent of said single pressure waves (SW[2].Mean_SWP−SW[1].Mean_SWP; FIG. 5b). The delta single pressure wave (ΔSW.x)-related parameters are all relative values.

[0258] Test recordings show that delta single pressure wave (ΔSW.x)-related parameters have an important role in the verification process assessing the quality of continuous pressure signals. In the presence of bad signal quality, the delta single pressure wave (ΔSW.x)-related parameters have greater values than in the presence of good signal quality.

[0259] Reference is now given to computation of time sequence (TS.x)-related parameters. In FIG. 6 is shown a continuous intracranial pressure (ICP) signal 601, including the included (i.e. accepted) single pressure waves 602. The two dimensions of a continuous pressure signal 601 are indicated by the pressure scale 603 and the time scale 604. One individual time sequence window 605 is shown, revealing that the selectable duration was 6 seconds in this case. It is illustrated that said selectable time sequence window 605 is a selected time frame of said sampled signal 601. Time sequence (TS.x)-related parameters are computed for each of said individual time sequence windows 605, based on included single pressure waves 602 within said individual time sequence window 605. For this particular time sequence window 605 there are seven included single pressure waves 602, indicated by the wave durations (WD) of said waves 602 (termed SW[1].WD, SW[2].WD, SW[3].WD, SW[4].WD, SW[5].WD, SW[6].WD, and SW[7].WD). For said included single pressure waves 602 are as well indicated the amplitudes (SW.dP) (termed SW[1].dP, SW[2].dP, SW[3].dP, SW[4].dP, SW[5].dP, SW[6].dP, and SW[7].dP). In addition is shown the systolic maximum pressure difference between two subsequent (n−1;n) single pressure waves (ΔSW.Diff_P_max) (termed ΔSW[1].Diff_P_max, ΔSW[2].Diff_P_max, ΔSW[3].Diff_P_max, ΔSW[4].Diff_P_max, ΔSW[5].Diff_P_max, and ΔSW[6].Diff_P_max,).

[0260] According to this invention there is identified a number of said time sequence (TS.x)-related parameters of said single pressure waves 602 during individual of said time sequence windows 605, said parameters selected from the group of:

• • [0261] mean value of starting diastolic minimum pressures of a time sequence window (TS.MeanP_min1), as further detailed in equation (17):

[0262] $\begin{array}{cc}\mathrm{TS}\left[i\right].{\mathrm{MeanP}}_{\mathrm{min1}}=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\mathrm{SW}\left[n\right].P_{\mathrm{min1}}.\mathrm{Value}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(17\right)\end{array}$

• • [0263] standard deviation of mean value of starting diastolic minimum pressures of a time sequence window (TS.MeanP_min1—STD), as further detailed in equation (18):

[0264] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{MeanP}}_{\mathrm{min1\_}}\mathrm{STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].P_{\mathrm{min1}}.\mathrm{Value}-\\ \mathrm{TS}\left[i\right].{\mathrm{MeanP}}_{\mathrm{min1}}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(18\right)\end{array}$

• • [0265] mean value of systolic maximum pressures of a time sequence window (TS.MeanP_max), as further detailed in equation (19):

[0266] $\begin{array}{cc}\mathrm{TS}\left[i\right].{\mathrm{MeanP}}_{\max }=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right]..\mathrm{SWCount}}\mathrm{SW}\left[n\right].P_{\max }.\mathrm{Value}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(19\right)\end{array}$

• • [0267] standard deviation of mean value of systolic maximum pressures of a time sequence window (TS.MeanP_max—STD), as further detailed in equation (20):

[0268] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{MeanP}}_{\mathrm{max\_}}\mathrm{STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].P_{\max .}\mathrm{Value}-\\ \mathrm{TS}\left[i\right].{\mathrm{MeanP}}_{\max }\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(20\right)\end{array}$

• • [0269] mean amplitude of a time sequence window (TS.MeandP), as further detailed in equation (21):

[0270] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeandP}=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right]..\mathrm{SWCount}}\mathrm{SW}\left[n\right].\mathrm{dP}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(21\right)\end{array}$

• • [0271] standard deviation of mean amplitude of a time sequence window (TS.MeandP_STD), as further detailed in equation (22):

[0272] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeandP\_STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].\mathrm{dP}-\\ \mathrm{TS}\left[i\right].\mathrm{MeandP}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(22\right)\end{array}$

• • [0273] mean latency of a time sequence window (TS.MeandT), as further detailed in equation (23):

[0274] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeandT}=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\mathrm{SW}\left[n\right].\mathrm{dT}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(23\right)\end{array}$

• • [0275] standard deviation of mean latency of a time sequence window (TS.MeandT_STD), as further detailed in equation (24):

[0276] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeandT\_STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].\mathrm{dT}-\\ \mathrm{TS}\left[i\right].\mathrm{MeandT}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(24\right)\end{array}$

• • [0277] mean rise time coefficient of a time sequence window (TS.MeanRT), as further detailed in equation (25):

[0278] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeanRT}=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\mathrm{SW}\left[n\right].\mathrm{RT}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(25\right)\end{array}$

• • [0279] standard deviation of mean rise time coefficient of a time sequence window (TS.MeanRT_STD), as further detailed in equation (26):

[0280] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeanRT\_STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].\mathrm{RT}-\\ \mathrm{TS}\left[i\right].\mathrm{MeanRT}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(26\right)\end{array}$

• • [0281] mean wave duration of a time sequence window (TS.MeanWD), as further detailed in equation (27):

[0282] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeanWD}=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\mathrm{SW}\left[n\right].\mathrm{WD}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(27\right)\end{array}$

• • [0283] standard deviation of mean wave duration of a time sequence window (TS.MeanWD_STD), as further detailed in equation (28):

[0284] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeanWD\_STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].\mathrm{WD}-\\ \mathrm{TS}\left[i\right].\mathrm{MeanWD}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(28\right)\end{array}$

• • [0285] mean single wave pressure of a time sequence window (TS.Mean_SWP), as further detailed in equation (29):

[0286] $\begin{array}{cc}\mathrm{TS}\left[i\right].{\mathrm{Mean}}_{\mathrm{SW}}P=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\mathrm{SW}\left[n\right].{\mathrm{Mean}}_{\mathrm{SW}}P}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(29\right)\end{array}$

• • [0287] standard deviation of mean single wave pressure of a time sequence window (TS.Mean_SWP_STD), as further detailed in equation (30):

[0288] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{Mean}}_{\mathrm{SW}}\mathrm{P\_STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].{\mathrm{Mean}}_{\mathrm{SW}}P-\\ \mathrm{TS}\left[i\right].{\mathrm{Mean}}_{\mathrm{SW}}P\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(30\right)\end{array}$

• • [0289] mean of diastolic minimum pressure differences of a time sequence window (TS.MeanDiff_P_min), as further detailed in equation (31):

[0290] $\begin{array}{cc}\mathrm{TS}\left[i\right].{\mathrm{MeanDiff\_P}}_{\min }=\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\mathrm{SW}\left[n\right].{\mathrm{Diff\_P}}_{\min }}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}& \left(31\right)\end{array}$

• • [0291] standard deviation of mean of diastolic minimum pressure differences of a time sequence window (TS.MeanDiff_P_min—STD), as further detailed in equation (32):

[0292] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{MeanDiff\_P}}_{\min }\mathrm{\_STD}\end{array}=\sqrt{\frac{\sum _{n=1}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\mathrm{SW}\left[n\right].{\mathrm{Diff\_P}}_{\min }-\\ \mathrm{TS}\left[i\right].{\mathrm{MeanDiff\_P}}_{\min }\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}}}& \left(32\right)\end{array}$

• • [0293] mean of systolic maximum pressure differences of a time sequence window (TS.MeanDiff_P_max), as further detailed in equation (33):

[0294] $\begin{array}{cc}\mathrm{TS}\left[i\right].{\mathrm{MeanDiff\_P}}_{\max }=\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\Delta \mathrm{SW}\left[n\right].P_{\max }.\mathrm{Value}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}& \left(33\right)\end{array}$

• • [0295] standard deviation of mean of systolic maximum pressure differences of a time sequence window (TS.MeanDiff_P_max—STD), as further detailed in equation (34):

[0296] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{MeanDiff\_P}}_{\max }\mathrm{\_STD}\end{array}=\sqrt{\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\Delta \mathrm{SW}\left[n\right].P_{\max }.\mathrm{Value}-\\ \mathrm{TS}\left[i\right].{\mathrm{MeanDiff\_P}}_{\max }\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}}& \left(34\right)\end{array}$

• • [0297] mean amplitude difference of a time sequence window (TS.MeanDiff_dP), as further detailed in equation (35):

[0298] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeanDiff\_dP}=\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\Delta \mathrm{SW}\left[n\right].\mathrm{dP}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}& \left(35\right)\end{array}$

• • [0299] standard deviation of mean amplitude difference of a time sequence window (TS.MeanDiff_dP_STD), as further detailed in equation (36):

[0300] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeanDiff\_dP}\mathrm{\_STD}\end{array}=\sqrt{\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\Delta \mathrm{SW}\left[n\right].\mathrm{dP}-\\ \mathrm{TS}\left[i\right].\mathrm{MeanDiff\_dP}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}}& \left(36\right)\end{array}$

• • [0301] mean latency difference of a time sequence window (TS.MeanDiff_dT), as further detailed in equation (37):

[0302] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeanDiff\_dT}=\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\Delta \mathrm{SW}\left[n\right].\mathrm{dT}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}& \left(37\right)\end{array}$

• • [0303] standard deviation of mean latency difference of a time sequence window (TS.MeanDiff_dT_STD), as further detailed in equation (38):

[0304] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeanDiff\_dT}\mathrm{\_STD}\end{array}=\sqrt{\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\Delta \mathrm{SW}\left[n\right].\mathrm{dT}-\\ \mathrm{TS}\left[i\right].\mathrm{MeanDiff\_dT}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}}& \left(38\right)\end{array}$

• • [0305] mean rise time coefficient difference of a time sequence window (TS.MeanDiff_RT), as further detailed in equation (39):

[0306] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeanDiff\_RT}=\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\Delta \mathrm{SW}\left[n\right].\mathrm{RT}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}& \left(39\right)\end{array}$

• • [0307] standard deviation of mean rise time coefficient difference of a time sequence window (TS.MeanDiff_RT_STD), as further detailed in equation (40):

[0308] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeanDiff\_RT}\mathrm{\_STD}\end{array}=\sqrt{\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\Delta \mathrm{SW}\left[n\right].\mathrm{RT}-\\ \mathrm{TS}\left[i\right].\mathrm{MeanDiff\_RT}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}}& \left(40\right)\end{array}$

• • [0309] mean wave duration difference of a time sequence window (TS.MeanDiff_WD), as further detailed in equation (41):

[0310] $\begin{array}{cc}\mathrm{TS}\left[i\right].\mathrm{MeanDiff\_WD}=\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\Delta \mathrm{SW}\left[n\right].\mathrm{WD}}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}& \left(41\right)\end{array}$

• • [0311] standard deviation of mean wave duration difference of a time sequence window (TS.MeanDiff_WD_STD), as further detailed in equation (42):

[0312] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ \mathrm{MeanDiff\_WD}\mathrm{\_STD}\end{array}=\sqrt{\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\Delta \mathrm{SW}\left[n\right].\mathrm{WD}-\\ \mathrm{TS}\left[i\right].\mathrm{MeanDiff\_WD}\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}}& \left(42\right)\end{array}$

• • [0313] mean single wave pressure difference of a time sequence window (TS.MeanDiff_Mean_SWP), as further detailed in equation (43):

[0314] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{MeanDiff\_Mean}}_{\mathrm{SW}}P\end{array}=\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}\Delta \mathrm{SW}\left[n\right].{\mathrm{Mean}}_{\mathrm{SW}}P}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}& \left(43\right)\end{array}$

• • [0315] standard deviation of mean single wave pressure difference of a time sequence window (TS.MeanDiff_Mean_SWP_STD), as further detailed in equation (44):

[0316] $\begin{array}{cc}\begin{array}{c}\mathrm{TS}\left[i\right].\\ {\mathrm{MeanDiff\_Mean}}_{\mathrm{SW}}\mathrm{P\_STD}\end{array}=\sqrt{\frac{\sum _{n=2}^{\mathrm{TS}\left[i\right].\mathrm{SWCount}}{\left(\begin{array}{c}\Delta \mathrm{SW}\left[n\right].{\mathrm{Mean}}_{\mathrm{SW}}P-\\ \mathrm{TS}\left[i\right].{\mathrm{MeanDiff\_Mean}}_{\mathrm{SW}}P\end{array}\right)}^2}{\mathrm{TS}\left[i\right].\mathrm{SWCount}-1}}& \left(44\right)\end{array}$

• • [0317] number of accepted single pressure waves of a time sequence window (TS.SWCount), wherein: TS[i].SWCount=Number of included single pressure waves within a time sequence window,
  • [0318] mean wave amplitude of a time sequence window computed according to the first matrix (TS.MeanWavedP) (see separate description with reference to FIG. 8),
  • [0319] mean wave latency of a time sequence window computed according to the first matrix (TS.MeanWavedT) (see separate description with reference to FIG. 8),
  • [0320] mean wave rise time coefficient of a time sequence window computed according to the second matrix (TS.MeanWaveRT)) (see separate description with reference to FIG. 8).

[0321] Mean amplitude during said time sequence window 605 (TS.MeandP) is the sum of amplitude values for all individual single pressure waves 602 during said time sequence window 605 divided by the number of single pressure waves 602 within said time sequence window 605. With reference to FIG. 6, the equation is as follows: TS.MeandP=(SW[1].dP+SW[2].dP+SW[3].dP+SW[4].dP+SW[5].dP+SW[6].dP+SW[7].dP)/7. Mean of systolic maximum pressure differences during a time sequence window (TS.MeanDiff_P_max) is the sum of systolic maximum pressure differences between two subsequent (n−1;n) single pressure waves (ΔSW.Diff_P_max) values when considering all individual single pressure waves 602 during said time sequence window 605 divided by the number of single pressure waves minus one 602 within said time sequence window 605. With reference to FIG. 6, the equation is as follows: TS.MeanDiff_P_max=(ΔSW[1].Diff_P_max+ΔSW[2].Diff_P_max+ΔSW[3].Diff_P_max+ΔSW[4].Diff_P_max+ΔSW[5].Diff_P_max+ΔSW[6].Diff_P_max)/6. It should be noted that with reference to ΔSW.Diff_P_max, the symbol d is used in FIG. 6 instead of the symbol Δ. Mean wave duration of a time sequence window (TS.MeanWD) is the sum of wave duration (SW.WD) values for all individual single pressure waves 602 during said time sequence window 605 divided by the number of single pressure waves 602 within said time sequence window 605. With reference to FIG. 6, the equation is as follows: TS.MeanWD=(SW[1].WD+SW[2].WD+SW[3].WD+SW[4].WD+SW[5].WD+SW[6].WD+SW[7].WD)/7.

[0322] Mean latency during said time sequence window 605 (TS.MeandT) is the sum of latency values for all individual single pressure waves 602 during said time sequence window 605 divided by the number of single pressure waves 602 within said time sequence window 605. Mean rise time coefficient during said time sequence window 605 (TS.MeanRT) is the sum of mean rise time coefficient values for all individual single pressure waves 602 during said time sequence window divided by the number of single pressure waves 602 within said time sequence window 605. Mean single wave pressure during said time sequence window 605 (TS.Mean_SWP) is the sum of absolute mean pressure, i.e. related to wave duration extending from SW.P_min1.Location to SW.P_min2.Location-1 for all individual single pressure waves 602 during said time sequence window 605 divided by the number of single pressure waves 602 within said time sequence window 605. It should be noted that mean single wave pressure (TS.Mean_SWP) relates to absolute pressure relative to atmospheric pressure.

[0323] Mean wave amplitude during said time sequence window 605 (TS.MeanWavedP) is computed according to a first matrix as balanced position in said matrix of number of occurrences of amplitude (SW.dP) and latency (SW.dT) values for all individual single pressure waves 602 during said time sequence window 605. Mean wave latency during said time sequence window (TS.MeanWavedT) is computed according to said first matrix as balanced position in said matrix number of occurrences of amplitude (SW.dP) and latency (SW.dT) values for all individual single pressure waves 602 during said time sequence window 605. Mean wave rise time coefficient during said time sequence window 605 (TS.MeanWaveRT) is computed according to a second matrix as balanced position in said second matrix of number of occurrences of rise time coefficient values (SW.RT) for all individual single pressure waves 602 during said time sequence window 605.

[0324] Mean wave duration difference during said time sequence window 605 (TS.MeanDiff_WD) is the mean value of all ΔSW.Diff_WD between subsequent single pressure waves 602 during said time sequence window 605 divided by numbers of single pressure waves 602 during said time sequence window 605. With reference to FIG. 6, the equation is as follows:
TS.MeanDiff_WD=[(SW[2].WD−SW[1].WD)+(SW[3].WD−SW[2].WD)+(SW[4].WD−SW[3].WD)+(SW[5].WD−SW[4].WD)+(SW[6].WD−SW[5].WD)+(SW[7].WD−SW[6].WD)]/6.

[0325] The time sequence (TS.x)-related parameters can be considered as the “building blocks” of the inventive method of processing continuous pressure-related signals, and represents a key element of the invention. Time sequence (TS.x) parameters computed from time sequence windows of good signal quality (i.e. single pressure waves are created by cardiac beat-induced pressure waves) provide for completely new information about pressure measurements, not revealed by current and prior art technology. These important significance areas relate to aspects such as e.g.:

• [0326] a) Quality control. Whether or not the pressure signal is good or bad is determined by TS.x-related parameters such as e.g. TS.MeanP_min1—STD, TS.MeanP_max—STD, TS.MeandP_STD, TS.MeandT_STD, TS.MeanWD, TS.MeanWD_STD, TS.MeanDiff_P_min and TS.SWCount.
• [0327] b) New diagnostic information. Whether or not pressures are abnormally high or not are determined by TS.x-related parameters such as e.g. TS.MeandP, TS.MeanRT, TS.Mean_SWP, TS.MeanWavedP, TS.MeanWavedT, and TS.MeanWaveRT. Such information is not derived from current and prior art technology.
• [0328] c) New pressure comparisons method. Two or more simultaneous signals constituting a pressure recording can be compared to determine relationships between said signals using said TS.x parameters such as e.g. TS.MeandP, TS.MeandP_STD, TS.MeanRT, TS.MeanRT_STD, TS.MeanDiff_dP, TS.MeanDiff_dP_STD.
• [0329] d) Relationship determination. Concerning the topic of processing signals derived from outside or inside a human or animal body cavity, it is crucial to determine relationships between TS.x-related parameters involving absolute or relative pressure values. Examples of TS.x-related parameters including absolute pressure values are such as TS.MeanP_min1, TS.MeanP_max, TS.Mean_SWP. Examples of TS.x-related parameters including relative pressure values are such as TS.MeandP, TS.MeanRT, TS.MeanDiff_dP and TS.MeanWavedP. In this context absolute pressure refers to pressure relative to atmospheric pressure. A challenge not solved by current and prior art technology is how to obtain useful diagnostic information from pressure measurements without a zero pressure level against atmospheric pressure. Computation of relationships between said TS.x-related parameters represents a technical solution to said challenge.

[0330] It is now focused on delta time sequence (ΔTS.x)-related parameters, with reference to FIG. 7a. First some remarks should be made concerning FIGS. 7a and 7b. In FIG. 7a is shown a continuous intracranial pressure (ICP) signal (Signal[1]) 701 derived from a sensor within the brain parenchyma (Location: Intra-dural), and in FIG. 7b is shown a continuous intracranial pressure (ICP) signal (Signal[2]) 702 derived from a sensor within the epidural space (Location: Epidural). Both these signals were derived from the same one recording Recording[62]; both signals were sampled simultaneously with identical time reference. The time (x-axis) 703 is identical both for Signal[1] 701 and Signal[2] 702. The pressure scale 704 of Signal[1] 701 (FIG. 7a) and the pressure scale 705 of Signal[2] 702 (FIG. 7b) had identical resolution though the absolute pressure levels were different. For both signals are shown two subsequent time sequence windows, termed Time Sequence[30] 706 (n−1) and Time Sequence[31] 707 (n).

[0331] For Signal[1] 701 the amplitudes (SW.dP) of the six included single pressure waves 708 within the first time sequence window (Time Sequence[30] 706) are numbered 709 (SW[1].dP), 710 (SW[2].dP), 711 (SW[3].dP), 712 (SW[4].dP), 713 (SW[5].dP), and 714 (SW[6].dP). For Signal[1] 701 the amplitudes (SW.dP) of the seven included single pressure waves 708 within the second time sequence window (Time Sequence[31] 707) are numbered 715 (SW[1].dP), 716 (SW[2].dP), 717 (SW[3].dP), 718 (SW[4].dP), 719 (SW[5].dP), 720 (SW[6].dP) and 721 (SW[7].dP). For Signal[2] 702 the amplitudes (SW.dP) of the six included single pressure waves 708′ within the first time sequence window (Time Sequence[30] 706) are numbered 722 (SW[1].dP), 723 (SW[2].dP), 724 (SW[3].dP), 725 (SW[4].dP), 726 (SW[5].dP), and 727 (SW[6].dP). For Signal[2] 702 the amplitudes (SW.dP) of the seven included single pressure waves 708′ within the second time sequence window (Time Sequence[31] 707) are numbered 728 (SW[1].dP), 729 (SW[2].dP), 730 (SW[3].dP), 731 (SW[4].dP), 732 (SW[5].dP), 733 (SW[6].dP) and 734 (SW[7] .dP).

[0332] As previously commented on FIG. 4a and 4b, a subgroup of selected Time Sequence and Delta Time Sequence Criteria define whether single pressure waves 708, 708′ which occur between two consecutive of said time sequence windows [Time Sequence[30] 706 (n−1) versus Time Sequence[31] 707 (n)] are to be placed within one or the other of said two consecutive individual time sequence windows (706, 707). Said selected criteria define that a first of said single pressure waves (SW[1] 715; FIG. 7a) 708 within said Time Sequence[31] 707 must have its ending diastolic minimum pressure value (SW.P_min2) within said Time Sequence[31] 707. Said selected criteria also define that a last of said single pressure waves (SW[7] 721; FIG. 7a) 708 within said Time Sequence[31] 707 must have both its starting diastolic minimum pressure value (SW.P_min1) and its ending diastolic minimum pressure value (SW.P_min2) within said Time Sequence[31] 707. These criteria are intended to illustrate the concept of the invention, not to limit the scope thereof, as other criteria may be used as well or as a replacement.

[0333] Reference is now given to FIG. 7a to illustrate the concept of computing delta time sequence (ΔTS.x)-related parameters between subsequent time sequences windows (n/n−1), said parameters are selected from the group of:

• • [0334] difference of mean values of starting diastolic minimum pressures between two subsequent time sequence windows (ΔTS.MeanP_min1), as further detailed in equation (45):
    ΔTS[i].MeanP_min1=TS[i].MeanP_min1−TS[i−1].MeanP_min1  (45)
  • [0335] standard deviation of difference of mean values of starting diastolic minimum pressures of two subsequent time sequence windows (ΔTS.MeanP_min1—STD), as further detailed in equation (46):
    ΔTS[i].MeanP_min1—STD=TS[i].MeanP_min1—STD−TS[i−1].MeanP_min1—STD  (46)
  • [0336] difference of mean values of systolic maximum pressure between two time sequence windows (ΔTS.MeanP_max), as further detailed in equation (47):
    ΔTS[i].MeanP_max=TS[i].MeanP_max−TS[i−1].MeanP_max  (47)
  • [0337] standard deviation of difference of mean values of systolic maximum pressure between two subsequent time sequence windows (ΔTS.MeanP_max—STD), as further detailed in equation (48):
    ΔTS[i].MeanP_max—STD=TS[i].MeanP_max—STD−TS[i−1].MeanP_max—STD  (48)
  • [0338] difference of mean amplitude values between two subsequent time sequence windows (ΔTS.MeandP), as further detailed in equation (49):
    ΔTS[i].MeanP=TS[i].MeanP−TS[i−1].MeanP  (49)
  • [0339] standard deviation of difference of mean amplitudes between two subsequent time sequence windows (ΔTS.MeandP_STD), as further detailed in equation (50):
    ΔTS[i].MeanP_—STD=TS[i].MeanP_—STD−TS[i−1].MeanP_—STD  (50)
  • [0340] difference of mean latency between two subsequent time sequence windows (ΔTS.MeandT), as further detailed in equation (51):
    ΔTS[i].MeandT=TS[i].MeandT−TS[i−1].MeandT  (51)
  • [0341] standard deviation of difference of mean latency between two subsequent time sequence windows (ΔTS.MeandT_STD), as further detailed in equation (52):
    ΔTS[i].MeandT_—STD=TS[i].MeandT_—STD−TS[i−1].MeandT_—STD  (52)
  • [0342] difference of mean rise time coefficient between two subsequent time sequence windows (ΔTS.MeanRT), as further detailed in equation (53):
    ΔTS[i].MeanRT=TS[i].MeanRT−TS[i−1].MeanRT  (53)
  • [0343] standard deviation of difference of mean rise time coefficient between two subsequent time sequence windows (ΔTS.MeanRT_STD), as further detailed in equation (54):
    ΔTS[i].MeanRT_—STD=TS[i].MeanRT_—STD−TS[i−1].MeanRT_—STD  (54)
  • [0344] difference of mean wave duration between two subsequent time sequence windows (ΔTS.MeanWD), as further detailed in equation (55):
    ΔTS[i].MeanWD=TS[i].MeanWD−TS[i−1].MeanWD  (55)
  • [0345] standard deviation of difference of mean wave duration between two subsequent time sequence windows (ΔTS.MeanWD_STD), as further detailed in equation (56):
    ΔTS[i].MeanWD_—STD=TS[i].MeanWD_—STD−TS[i−1].MeanWD_—STD  (56)
  • [0346] difference of mean single wave pressure between two subsequent time sequence windows (ΔTS.Mean_SWP), as further detailed in equation (57):
    ΔTS[i].Mean_SWP=TS[i].Mean_SWP−TS[i−1].Mean_SWP  (57)
  • [0347] standard deviation of difference of mean single wave pressure of two subsequent time sequence windows (ΔTS.Mean_SWP_STD), as further detailed in equation (58):
    ΔTS [i].Mean_swP_—STD=TS[i]. Mean_swP_—STD−TS[i−1]. Mean_swP_—STD  (58)
  • [0348] difference of mean diastolic minimum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_min), as further detailed in equation (59):
    ΔTS[i].MeanDiff_—P_min=TS[i].MeanDiff_—P_min−TS[i−1].MeanDiff_—P_min  (59)
  • [0349] standard deviation of difference of mean diastolic minimum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_min—STD), as further detailed in equation (60):
    ΔTS[i].MeanDiff_—P_min—STD=TS[i].MeanDiff_—P_min—STD−TS[i−1].MeanDiffP_min—STD  (60)
  • [0350] difference of mean systolic maximum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_max), as further detailed in equation (61):
    ΔTS[i].MeanDiff_—P_max=TS[i].MeanDiff_—P_max−TS[i−1].MeanDiff_—P_max  (61)
  • [0351] standard deviation of difference of mean systolic maximum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_max—STD), as further detailed in equation (62):
    ΔTS[i].MeanDiffP_max—STD=TS[i].MeanDiffP_max—STD−TS[i−1].MeanDiffP_min—STD  (62)
  • [0352] difference of mean amplitude difference between two subsequent time sequence windows (ΔTS.MeanDiff_dP), as further detailed in equation (63):
    ΔTS[i].MeanDiff_—dP=TS[i].MeanDiff_—dP−TS[i−1].MeanDiff_—dP  (63)
  • [0353] standard deviation of difference of mean amplitude difference between two subsequent time sequence windows (ΔTS.MeanDiff_dP_STD), as further detailed in equation (64):
    ΔTS[i].MeanDiffdP_—STD=TS[i].MeanDiffdP_—STD−TS[i−1].MeanDiffdP_—STD  (64)
  • [0354] difference of mean latency difference between two subsequent time sequence windows (ΔTS.MeanDiff_dT), as further detailed in equation (65):
    ΔTS[i].MeanDiff_—dT=TS[i].MeanDiff_—dT−TS[i−1].MeanDiff_—dT  (65)
  • [0355] standard deviation of difference of mean latency difference between two subsequent time sequence windows (ΔTS.MeanDiff_dT_STD), as further detailed in equation (66):
    ΔTS[i].MeanDiff_—dT_—STD=TS[i].MeanDiff_—dT_—STD−TS[i−1].MeanDiff_—dT_—STD  (66)
  • [0356] difference of mean rise time coefficient difference between two subsequent time sequence windows (ΔTS.MeanDiff_RT), as further detailed in equation (67):
    ΔTS[i].MeanDiff_—RT=TS[i].MeanDiff_—RT−TS[i−1].MeanDiff_—RT  (67)
  • [0357] standard deviation of difference of mean rise time coefficient difference between two subsequent time sequence windows (ΔTS.MeanDiff_RT_STD), as further detailed in equation (68):
    ΔTS[i].MeanDiff_—RT_—STD TS[i].MeanDiff_—RT_—STD−TS[i−1].MeanDiff_—RT_—STD  (68)
  • [0358] difference of mean wave duration difference between two subsequent time sequence windows (ΔTS.MeanDiff_WD), as further detailed in equation (69):
    ΔTS[i].MeanDiff_—WD=TS[i].MeanDiff_—WD_—TS[i−1].MeanDiff_—WD  (69)
  • [0359] standard deviation of difference of mean wave duration difference between two subsequent time sequence windows (ΔTS.MeanDiff_WD_STD), as further detailed in equation (70):
    ΔTS[i].MeanDiffWD_—STD=TS[i].MeanDiffWD_—STD−TS[i−1].MeanDiffWD_—STD  (70)
  • [0360] difference of mean single wave pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_Mean_SWP), as further detailed in equation (71):
    ΔTS[i].MeanDiff_Mean_swP=TS[i].MeanDiff_Mean_swP−TS[i−1].MeanDiff_Mean_swP  (71)
  • [0361] standard deviation of difference of mean SW pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_Mean_SWP_STD), as further detailed in equation (72):
    ΔTS[i].MeanDiff_Mean_SWP_—STD=TS[i].MeanDiff_Mean_SWP_—STD−TS[i−1].MeanDiff_Mean_SWP_—STD  (72)
  • [0362] difference of single wave count between two subsequent time sequence windows (ΔTS.SWCount), as further detailed in equation (73):
    ΔTS[i].SWCount=TS[i].SWCount−TS[i−1].SWCount  (73)
  • [0363] difference of mean wave amplitude between two subsequent time sequence windows (ΔTS.MeanWavedP), as further detailed in equation (74):
    ΔTS[i].MeanWavedP=TS[i].MeanWavedP−TS[i−1].MeanWavedP  (74)
  • [0364] difference of mean wave latency between two subsequent time sequence windows (ΔTS.MeanWavedT), as further detailed in equation (75):
    ΔTS[i].MeanWavedT=TS[i].MeanWavedT−TS[i−1].MeanWavedT  (75)
  • [0365] difference of mean wave rise time coefficient between two subsequent time sequence windows (ΔTS.MeanWaveRT), as further detailed in equation (76):
    ΔTS[i].MeanWaveRT=TS[i].MeanWaveRT−TS[i−1].MeanWaveRT  (76)

[0366] To further illustrate the concept it is shown the computation of difference of mean amplitude values between two subsequent time sequences (ΔTS.MeandP).

[0367] With reference to Signal[1] 701 the equation is as follows: ΔTS.MeanP=TS[31].MeanP−TS[30].MeanP. Again, TS[31].x refers to Time Sequence[31] 707 and TS[1].x refers to Time Sequence[30] 706. With reference to FIG. 7a, the equation for TS[30].MeandP within Time Sequence[30] 706 is as follows: TS[30].MeandP=(SW[1].dP+SW[2].dP+SW[3].dP +SW[4].dP+SW[5].dP+SW[6].dP)/6 (wherein SW[1].dP=709; SW[2].dP=710; SW[3].dP =711; SW[4].dP=712; SW[5].dP=713; SW[6].dP=714). With reference to FIG. 7a, the equation for TS[31].MeandP within Time Sequence[31] 707 is as follows: TS[31].MeandP=(SW[1].dP+SW[2].dP+SW[3].dP+SW[4].dP+SW[5].dP+SW[6].dP+SW[7].dP)/7 (wherein SW[1].dP=715; SW[2].dP=716; SW[3].dP=717; SW[4].dP=718; SW[5].dP=719; SW[6].dP=720; SW[7].dP=721).

[0368] Concerning the inventive step of computing said delta time sequence (ΔTS.x)-related parameters, the major significance relates to quality control of signal quality. The thresholds and ranges of said ΔTS.x parameters are different in the presence of good signal quality (i.e. single pressure waves are created by cardiac beat-induced pressure waves) as compared to bad signal quality (i.e. pressure waves are created by artifacts or a combination of artifacts or cardiac beat induced pressure waves). Said delta time sequence (ΔTS.x)-related parameters have a key role in excluding (i.e. not accepting) time sequence windows with said bad signal quality.

[0369] The strategy for best possible determination of single pressure waves created by cardiac beat-induced pressure waves, relates to the inventive step of determining criteria for the parameters (SW.x, ΔSW.x, TS.x, ΔTS.x ) related to time sequence windows of said continuous pressure-related signal. So-called criteria related to said parameters relate to thresholds and ranges of said parameters. By this inventive step now commented on in the subsequent paragraphs, a tool is created for best possible identification of single pressure waves created by cardiac beat-induced pressure waves

[0370] Reference is now given to said Determining Steps commented on for FIG. 2. With reference to the single wave (SW.x)-, delta single wave (ΔSW.x)-, time sequence (TS.x)- and delta time sequence (ΔTS.x)-related parameters, criteria are determined, referred to as Single Wave & Delta Single Wave Criteria and Time Sequence & Delta Time Sequence Criteria (see FIG. 2).

[0371] Single wave criteria relate to criteria for thresholds and ranges of said single pressure wave (SW.x)-related parameters of said single pressure waves during said time sequence windows, said parameters selected from the group of:

• • [0372] starting diastolic minimum pressure defining the start of the single pressure wave (SW.P_min1),
  • [0373] ending diastolic minimum pressure defining the end of the single pressure wave (SW.P_min2),
  • [0374] systolic maximum pressure of the single pressure wave (SW.P_max,),
  • [0375] amplitude of the single pressure wave (SW.dP),
  • [0376] latency of the single pressure wave (SW.dT),
  • [0377] rise time coefficient of the single pressure wave (SW.RT),
  • [0378] wave duration of the single pressure wave (SW.WD),
  • [0379] mean single wave pressure of the single pressure wave (SW.Mean_SWP), and
  • [0380] diastolic minimum pressure difference of the single pressure wave (SW.Diff_P_min).

[0381] In general, the notation related to criteria is:

• [0382] Criteria_Type_Location_Parameter. Some examples of Single Wave Criteria are given, which are specifically related to continuous intracranial pressure (ICP) signals as indicated in the notation:
  • [0383] Criteria_ICP_Intra-dural_SW.P_max:
    • [0384] a) Systolic maximum pressure of a single pressure wave must be between −5 to 100 mmHg for pair combinations of SW.P_min1 and SW.P_max to be included for further analysis.
  • [0385] Criteria_ICP_Intra-dural_SW.dP:
    • [0386] a) Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for pair combinations of SW.P_min1 and SW.P_max to be included for further analysis.
  • [0387] Criteria_ICP_Intra-dural_SW.dT:
    • [0388] a) Latency (SW.dT) must be between 0.10 to 0.40 seconds for pair combinations of SW.P_min1 and SW.P_max to be included for further analysis.
  • [0389] Criteria_ICP_Intra-dural_SW.WD:
    • [0390] a) Wave duration (SW.WD) must be between 0.30 to 1.5 seconds for pair combinations of SW.P_min1 and SW.P_max to be included for further analysis.

[0391] These examples are not intended to limit the scope of the invention; but to illustrate the substance of said Single Wave Criteria. As indicated, the criteria are related to thresholds and ranges of said single pressure wave (SW.x)-related parameters during said time sequence windows. Said criteria determine inclusion, i.e. acceptance, or exclusion, i.e. rejection, of single pressure waves for further analysis; for example minimum-maximum pressure (SW.P_min1/SW.P_max) pairs with said single pressure wave (SW.x)-related parameters outside selectable thresholds and ranges are excluded (see FIG. 2).

[0392] Determining criteria for thresholds and ranges of delta single pressure wave (ΔSW.x)-related parameters between subsequent of said single pressure waves during said time sequence windows refers to Delta Single Wave Criteria, said criteria are derived from the group of ΔSW.x-related parameters of:

• • [0393] systolic maximum pressure difference between two subsequent single pressure waves (ΔSW.Diff_P_max),
  • [0394] amplitude difference between two subsequent single pressure waves (ΔSW.Diff_dP),
  • [0395] latency difference between two subsequent single pressure waves (SW.Diff_dT),
  • [0396] rise time coefficient difference between two subsequent single pressure waves (ΔSW.Diff_RT),
  • [0397] wave duration difference between two subsequent single pressure waves (ΔSW.Diff_WD), and
  • [0398] mean single wave pressure difference between two subsequent single pressure waves (ΔSW.Diff_Mean_SWP).

[0399] Some examples of Delta Single Wave Criteria are given, which are specifically related to continuous intracranial pressure (ICP) signals as indicated in the notation:

• • [0400] Criteria_ICP_Intra-dural_ΔSW.P_max:
    • [0401] a) Systolic maximum pressure difference between two subsequent single pressure waves must be ≦10 mmHg for combinations of SW.P_min1 and SW.P_max to be included for further analysis.
  • [0402] Criteria_ICP_Intra-dural_ΔSW.WD:
    • [0403] a) Wave duration difference between two subsequent single pressure waves must be ≦0.10 seconds for pair combinations of SW.P_min1 and SW.P_max to be included for further analysis.

[0404] These examples are not intended to limit the scope of the invention; but to illustrate the substance of said Delta Single Wave Criteria. Said criteria for thresholds and ranges of said delta single pressure wave (ΔSW.x)-related parameters between subsequent of said single pressure waves during said time sequence windows determines inclusion or exclusion of said single pressure waves for further analysis. Said criteria exclude minimum-maximum pressure (SW.P_min1/SW.P_max) pairs with said delta single pressure wave (ΔSW.x)-related parameters outside selectable thresholds and ranges.

[0405] Determining criteria for thresholds and ranges of time sequence (TS.x)-related parameters of said single pressure waves during said time sequence windows refers to Time Sequence Criteria; said criteria are derived from the group of TS.x-related parameters of:

• • [0406] mean value of starting diastolic minimum pressures of a time sequence window (TS.MeanP_min1),
  • [0407] standard deviation of mean value of starting diastolic minimum pressures of a time sequence window (TS.MeanP_min1—STD),
  • [0408] mean value of systolic maximum pressures of a time sequence window (TS.MeanP_max),
  • [0409] standard deviation of mean value of systolic maximum pressures of a time sequence window (TS.MeanP_max—STD),
  • [0410] mean amplitude of a time sequence window (TS.MeandP),
  • [0411] standard deviation of mean amplitude of a time sequence window (TS.MeandP_STD),
  • [0412] mean latency of a time sequence window (TS.MeandT),
  • [0413] standard deviation of mean latency of a time sequence window (TS.MeandT_STD),
  • [0414] mean rise time coefficient of a time sequence window (TS.MeanRT),
  • [0415] standard deviation of mean rise time coefficient of a time sequence window (TS.MeanRT_STD),
  • [0416] mean wave duration of a time sequence window (TS.MeanWD),
  • [0417] standard deviation of mean wave duration of a time sequence window (TS.MeanWD_STD),
  • [0418] mean single wave pressure of a time sequence window (TS.Mean_SWP),
  • [0419] standard deviation of mean single wave pressure of a time sequence window (TS.Mean_SWP_STD),
  • [0420] mean of diastolic minimum pressure differences of a time sequence window (TS.MeanDiff_P_min),
  • [0421] standard deviation of mean of diastolic minimum pressure differences of a time sequence window (TS.MeanDiff_P_min—STD),
  • [0422] mean of systolic maximum pressure differences of a time sequence window (TS.MeanDiff_P_max),
  • [0423] standard deviation of mean of systolic maximum pressure differences of a time sequence window (TS.MeanDiff_P_max—STD),
  • [0424] mean amplitude difference of a time sequence window (TS.MeanDiff_dP),
  • [0425] standard deviation of mean amplitude difference of a time sequence window (TS.MeanDiff_dP_STD),
  • [0426] mean latency difference of a time sequence window (TS.MeanDiff_dT),
  • [0427] standard deviation of mean latency difference of a time sequence window (TS.MeanDiff_dT_STD),
  • [0428] mean rise time coefficient difference of a time sequence window (TS.MeanDiff_RT),
  • [0429] standard deviation of mean rise time coefficient difference of a time sequence window (TS.MeanDiff_RT_STD),
  • [0430] mean wave duration difference of a time sequence window (TS.MeanDiff_WD),
  • [0431] standard deviation of mean wave duration difference of a time sequence window (TS.MeanDiff_WD_STD),
  • [0432] mean single wave pressure difference of a time sequence window (TS.MeanDiff_Mean_SWP),
  • [0433] standard deviation of mean single wave pressure difference of a time sequence window (TS.MeanDiff_Mean_SWP_STD),
  • [0434] number of included single pressure waves of a time sequence window (TS.SWCount),
  • [0435] mean wave amplitude of a time sequence window (TS.MeanWavedP),
  • [0436] mean wave latency of a time sequence window (TS.MeanWavedT), and
  • [0437] mean wave rise time coefficient of a time sequence window (TS.MeanWaveRT).

[0438] Some examples of Time Sequence Criteria are given, which are specifically related to continuous intracranial pressure (ICP) signals as indicated in the notation:

• • [0439] Criteria_ICP_Intra-dural_TS.SWCount:
    • [0440] a) Number of included single pressure waves of a time sequence window must be between 4 and 18 for said time sequence window to be included (accepted) for further analysis.
  • [0441] Criteria_ICP_Intra-dural_TS.MeanP_max:
    • [0442] a) Mean value of systolic maximum pressures of a time sequence window must be between 2 and 100 mmHg for said time sequence window to be included for further analysis.

[0443] The intention of these examples of Time Sequence Criteria are to illustrate the substance of said criteria, though the specific values are not intended to limit the scope of the invention. Time Sequence Criteria for thresholds and ranges of time sequence (TS.x)-related parameters of single pressure waves during time sequence windows determines inclusion, i.e. acceptance, or exclusion, i.e. rejection, of said time sequences for further analysis. The criteria exclude for further analysis time sequences with time sequence (TS.x)-related parameters outside selectable thresholds and ranges.

[0444] Determining criteria for thresholds and ranges of delta time sequence (ΔTS.x)-related parameters between subsequent time sequences refers to Delta Time Sequence Criteria; said criteria are derived from the group of ΔTS.x-related parameters of:

• • [0445] difference of mean values of starting diastolic minimum pressures between two subsequent time sequence windows (ΔTS.MeanP_min1),
  • [0446] standard deviation of difference of mean values of starting diastolic minimum pressures of two subsequent time sequence windows (ΔTS.MeanP_min1—STD),
  • [0447] difference of mean values of systolic maximum pressure between two subsequent time sequence windows (ΔTS.MeanP_max),
  • [0448] standard deviation of difference of mean values of systolic maximum pressure between two subsequent time sequence windows (ΔTS.MeanP_max—STD),
  • [0449] difference of mean amplitude values between two subsequent time sequence windows (ΔTS.MeandP),
  • [0450] standard deviation of difference of mean amplitudes between two subsequent time sequence windows (ΔTS.MeandP_STD),
  • [0451] difference of mean latency between two subsequent time sequence windows (ΔTS.MeandT),
  • [0452] standard deviation of difference of mean latency between two subsequent time sequence windows (ΔTS.MeandT_STD),
  • [0453] difference of mean rise time coefficient between two subsequent time sequence windows (ΔTS.MeanRT),
  • [0454] standard deviation of difference of mean rise time coefficient between two subsequent time sequence windows (ΔTS.MeanRT_STD),
  • [0455] difference of mean wave duration between two subsequent time sequence windows (ΔTS.MeanWD),
  • [0456] standard deviation of difference of mean wave duration between two subsequent time sequence windows (ΔTS.MeanWD_STD),
  • [0457] difference of mean single wave pressure between two subsequent time sequence windows (ΔTS.Mean_SWP),
  • [0458] standard deviation of difference of mean single wave pressure of two subsequent time sequence windows (ΔTS.Mean_SWP_STD),
  • [0459] difference of mean diastolic minimum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_min),
  • [0460] standard deviation of difference of mean diastolic minimum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_min—STD),
  • [0461] difference of mean systolic maximum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_max),
  • [0462] standard deviation of difference of mean systolic maximum pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_P_min—STD),
  • [0463] difference of mean amplitude difference between two subsequent time sequence windows (ΔTS.MeanDiff_dP),
  • [0464] standard deviation of difference of mean amplitude difference between two subsequent time sequence windows (ΔTS.MeanDiff_dP_STD),
  • [0465] difference of mean latency difference between two subsequent time sequence windows (ΔTS.MeanDiff_dT),
  • [0466] standard deviation of difference of mean latency difference between two subsequent time sequence windows (ΔTS.MeanDiff_dT_STD),
  • [0467] difference of mean rise time coefficient difference between two subsequent time sequence windows (ΔTS.MeanDiff_RT),
  • [0468] standard deviation of difference of mean rise time coefficient difference between two subsequent time sequence windows (ΔTS.MeanDiff_RT_STD),
  • [0469] difference of mean wave duration difference between two subsequent time sequence windows (ΔTS.MeanDiff_WD),
  • [0470] standard deviation of difference of mean wave duration difference between two subsequent time sequence windows (ΔTS.MeanDiff_WD_STD),
  • [0471] difference of mean single wave pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_Mean_SWP),
  • [0472] standard deviation of difference of mean SW pressure difference between two subsequent time sequence windows (ΔTS.MeanDiff_Mean_SWP_STD),
  • [0473] difference of single wave count between two subsequent time sequence windows (ΔTS.SWCount),
  • [0474] difference of mean wave amplitude between two subsequent time sequence windows (ΔTS.MeanWavedP),
  • [0475] difference of mean wave latency between two subsequent time sequence 10 windows (ΔTS.MeanWavedT), and
  • [0476] difference of mean wave rise time coefficient between two subsequent time sequence windows (ΔTS.MeanWaveRT).

[0477] Some examples of Delta Time Sequence Criteria are given, which are specifically related to continuous intracranial pressure (ICP) signals as indicated in the notation:

• • [0478] Criteria_ICP_Intra-dural_ΔTS.SWCount:
    • [0479] a) Difference of single wave count between two subsequent time sequences must be ≦2 for said time sequence window to be included for further analysis.
  • [0480] Criteria_ICP_Intra-dural_ΔTS.MeanDiff_dP:
    • [0481] a) Difference of mean amplitude between two subsequent time sequences must be ≦5 mmHg for said time sequence window to be included for further analysis.

[0482] The intention of these examples of Delta Time Sequence Criteria are to illustrate the substance of said criteria, though the specific values are not intended to limit the scope of the invention. Delta Time Sequence Criteria for thresholds and ranges of time sequence (ΔTS.x)-related parameters between subsequent time sequence windows determine inclusion or exclusion of said time sequences for further analysis. The criteria exclude time sequence windows with said delta time sequence (ΔTS.x)-related parameters outside selectable thresholds and ranges.

[0483] It is differentiated between static and dynamic criteria. Static criteria for thresholds and ranges of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters are unchangeable during a recording. Dynamic criteria for said thresholds and ranges of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters are changeable during a recording.

[0484] The major application of determination of Single Wave & Delta Single Wave Criteria and Time Sequence & Delta Time Sequence Criteria are optimal identification of single pressure waves related to cardiac beat-induced pressure waves and identification of pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves. This represents an optimal differentiating between single pressure waves caused by cardiac beat-induced pressure waves and pressure waves caused by artifact-induced pressure waves or a combination thereof.

[0485] The determination of Single Wave & Delta Single Wave Criteria and Time Sequence & Delta Time Sequence Criteria should be considered an iterative process. This is explained with reference to the experience of the inventor. When analyzing continuous intracranial pressure (ICP) signals during the first years, the inventor used only the following criteria (i.e. Single Wave Criteria and Time Sequence Criteria):

• • [0486] Criteria_ICP_Intra-dural_SW.dP:
    • [0487] a) Amplitude (SW.dP) must be between 1.0 to 35.0 mmHg for pair combinations of SW.P_min1 and SW.P_max to be included for further analysis.
  • [0488] Criteria_ICP_Intra-dural SW.dT:
    • [0489] a) Latency (SW.dT) must be between 0.10 to 0.40 seconds for pair combinations of SW.P_min and SW.P_max to be included for further analysis.
  • [0490] Criteria_ICP_Intra-dural TS.SWCount:
    • [0491] a) Number of included single pressure waves of a time sequence window must be between 4 and 18 for said time sequence window to be included for further analysis.

[0492] Thus, with reference to the Identifying Steps shown in FIG. 2., included single pressure waves were identified by SW.P_max/SW.P_min1 pair combinations wherein the calculated amplitude (SW.dP) values were between 1.0 and 35.0 mmHg, and the calculated latency (SW.dT) values were between 0.10 and 0.40 seconds. Furthermore, the included time sequence windows contained between 4 and 18 included single pressure waves. Several hundreds of continuous intracranial pressure (ICP) recordings were obtained and stored in the database as raw data files by this approach. For each of these continuous intracranial pressure (ICP) recordings, the different SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters were computed. The method of computing said parameters is applied to each of said time sequence windows in a continuous series of said time sequence windows during a recording. By this approach a reference material is established concerning the normal distribution of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters. Based on this normal distribution, new and improved criteria are determined for thresholds and ranges of said SW.x-, ΔSW.x-, TS.x- and ΔTS.x-related parameters during said time sequence windows (or more correctly between said time sequence windows for ΔTS.x-related parameters). The new thresholds and ranges of said Single Wave & Delta Single Wave Criteria and Time Sequence & Delta Time Sequence Criteria are applied when analyzing new continuous intracranial pressure (ICP) signals. In addition, the raw data files of continuous intracranial pressure (ICP) signals stored in said database may be run through the Identifying Steps and Computing Steps shown in FIG. 2 once again, and both manual and automatic verification of the output being performed.

[0493] The Identifying Steps, Computing Steps and Determining Steps described in FIG. 2 provide a new and inventive method for processing continuous pressure-related signals derived from locations inside or outside a human or animal body or body cavity. Said inventive method (referred to as a first feature of this invention and further detailed in independent claim 1 and subsequent dependent sub-claims 2-58) has great significance as shortly summarized below:

[0494] Time sequence (TS.x)-related parameters can be computed from included time sequence windows wherein said time sequence windows are included the best possible way. Said time sequence windows contain single pressures waves related to cardiac beat-induced pressure waves, with less impact of pressure waves caused by artifacts or a combination of artifacts and cardiac beat-induced pressure waves. Thereby the risk of computing false or misleading time sequence (TS.x)-related parameters is made minimal.

[0495] Related time sequence (rTS.x) parameters can be determined from included time sequence windows wherein said time sequence windows are included the best possible way. Time sequence windows from simultaneous signals within a recording contain single pressure waves related to cardiac beat-induced pressure waves, with less impact of pressure waves created by artifacts or a combination of artifacts and cardiac beat-induced pressure waves. Obviously, it has no meaning to compute related time sequence (rTS.x) parameters from time sequence windows containing a high proportion of artifacts.

[0496] The method of computing time sequence (TS.x)-related parameters has a key role in other described devices and system according to this invention. The invention describes a display device(s), a sensor device (s) and combined sensor-display device(s) for use in sensing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity. The invention also describes a system for processing continuous pressure-related signals derivable from one or more sensor(s) having location(s) inside or outside a body or body cavity of a human being. As related to these features of the invention, it is crucial that time sequence (TS.x)-related parameters are computed from time sequence windows that are included the best possible way (i.e. time sequence windows containing single pressures waves related to cardiac beat-induced pressure waves, not to artifacts or a combination of artifacts and cardiac beat-induced pressure waves). Computation of false or misleading time sequence (TS.x)-related parameters would cause wrong measurements from said sensor device(s) and display wrong information from said display device (s) or combined sensor-display device(s).

[0497] According to the present invention there is provided for a device and a system, which enable inter alia more controlled drainage of excess fluid from a brain or spinal fluid cavity to another body cavity of a human being or animal. However, there are also aspects linked to the use of said device and system. As appreciated, said device and system include components making use of one or more of the inventive methods according to the invention to yield intended output from the device or system. A key element concerning these features of the invention is the computation of time sequence (TS.x)-related parameters. Obviously, it is crucial that time sequence (TS.x)-related parameters are computed from time sequence windows that are included the best possible way (i.e. time sequence windows containing single pressures waves related to cardiac beat-induced pressure waves, not to artifacts or a combination of artifacts and cardiac beat-induced pressure waves). Computation of false or misleading time sequence (TS.x)-related parameters would cause wrong regulation of drainage of fluid from said brain or spinal fluid cavity.

[0498] Reference is now given to the second feature of this invention. In particular, characteristics of said second feature are illustrated in FIG. 8, and also in FIGS. 4 and 7.

[0499] Said second feature of the invention is related to a method for processing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with a time reference. For selectable time sequence windows the method comprises the further steps of identifying from said digital data signal single pressure waves related to cardiac beat-induced pressure waves, and identifying from said digital data signal pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves. The method also relates to computing time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows, and establishing an analysis output selected from one or more of said time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows, said one or more parameters selectable from: Mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT), mean rise time coefficient (TS.MeanRT), and mean single wave pressure (TS.Mean_SWP).

[0500] Test recordings have shown that said time sequence (TS.x)-related parameters provide completely new information from continuous pressure-related signals, not revealed by current and prior art technology. Said new information is shortly summarized:

[0501] Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP, TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT, TS.Mean_SWP) provide completely new information whether pressures are abnormally high or not. For example, reduced cerebral compliance, i.e. abnormally high intracranial pressure (ICP), is not revealed by known technology, but is revealed by said TS.x parameters.

[0502] Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP, TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT, TS.Mean_SWP) provide completely new information whether a pressure measurement is of good or bad quality. For example, sensor error or wrong sensor placement may not always be detected by known technology, but are revealed by said TS.x parameters.

[0503] Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP, TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT, TS.Mean_SWP) provide completely new information whether pressures are changing during treatment (surgical or medical). A change in pressure may be impossible to reveal according to known and prior art technology. For example, a change in intracranial pressure (ICP) following medical or surgical treatment may be impossible to reveal by current technology, though pressure changes are shown by said TS.x parameters.

[0504] Time sequence (TS.x)-related parameters (e.g. such as TS.MeanWavedP, TS.MeanWavedT, TS.MeanWaveRT, TS.MeandP, TS.MeandT, TS.MeanRT) involving relative values are not influenced by a zero pressure level relative to atmospheric pressure or drift of sensor zero pressure level. This fact represents a technical solution as compared to current technology, in which such problems are important concerning calibration against a zero pressure level or drift of sensor pressure level.

[0505] An overview of said method for analyzing continuous pressure-related signals 801 derived from within or outside a human or animal body or body cavity is given in FIG. 8, and reference is now given to FIG. 8.

[0506] In short, the method incorporates different Identifying Steps, Computing Step and Presentation Step. Said Identifying Steps include identification of all separate peaks and valleys 802 in said sampled signal 801. Each of said peaks is a sample with a pressure value and a time stamp or location, and each of said valleys is a sample with a pressure value and a time stamp or location. The result of applying General Methods Criteria 803 is either included, i.e. accepted, peak/valley pair combinations 804 or excluded, i.e. rejecte, peak/valley pair combinations 805.

[0507] After applying the Single Wave & Delta Single Wave Criteria 806 to said included peak/valley pairs 804, the output is either included single pressure waves 807 or excluded pressure waves 808. Said criteria 806 relate to thresholds and ranges of single pressure wave (SW.x)-related parameters and delta single pressure wave (ΔSW.x)-related parameters during time sequence windows.

[0508] After applying the Single Wave & Delta Single Wave Criteria 806, included pair combinations of peak/valley pairs 804 in said signal 801 correspond to included single pressure waves 807. Pair combinations of diastolic minimum pressure (SW.P_min1) and systolic maximum pressure (SW.P_max) characterize single pressure waves created by cardiac beat-induced pressure waves. Said criteria 806 exclude for further analysis pressure waves (i.e. minimum-maximum pressure (SW.P_min1/SW.P_max) pairs) during said time sequence windows with said single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters outside selected criteria for thresholds and ranges of said parameters. Said criteria 806 include for further analysis single pressure waves 807 having single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters within selected criteria for thresholds and ranges of said single pressure wave (SW.x)-related parameters. Pair combinations of diastolic minimum pressure (SW.P_min1) and systolic maximum pressure (SW.P_max) correspond to diastolic minimum pressures and systolic maximum pressures of individual of pressure waves created by each of said cardiac beats.

[0509] In order to further evaluate the included single pressure waves 807, Time Sequence & Delta Time Sequence Criteria 809 are applied to each of said time sequence windows in a continuous series of said time sequence windows during a recording. Each time sequence window is a selected time frame of said signal. Said criteria 809 for thresholds and ranges of said time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters determine included time sequence windows 810 and excluded time sequence windows 811. Said criteria 809 exclude for further analysis time sequence windows 811 with time sequence (TS.x)-and delta time sequence (ΔTS.x)-related parameters outside selected criteria for thresholds and ranges of said parameters. Said criteria 809 include for further analysis time sequence windows 810 having time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters within selected criteria for thresholds and ranges of said time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters.

[0510] In a next Computing Step is computed time sequence (TS.x)-related parameters 812 for each individual of said included time sequence windows 810. The Identifying Steps are applied to each of said time sequence windows in a continuous series of said time sequence windows during a recording. The Computing Step is applied to each of said included time sequence windows 810 in a continuous series of said time sequence windows during a recording. In a final Presentation Step, an analysis output is established selected from one or more of said time sequence (TS.x)-related parameters: Mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT), mean rise time coefficient (TS.MeanRT), and mean single wave pressure (TS.Mean_SWP). Said analysis output for selected ones of said time sequence (TS.x)-related parameters includes different types of data presentation, such as repetitive numerical presentation, trend plot presentation, histogram presentation, and quantitative matrix presentation.

[0511] Concerning the Identifying Steps, the process method is identical as the process described for FIGS. 2, 3a, 3b, 3c, 4a, 4b, 5a, 5b, 6, and 7a. Details about General Methods Criteria 803, Single Wave & Delta Wave Criteria 806 and Time Sequence & Delta Time Sequence Criteria 809 are already commented on in detail with reference to FIG. 2, and therefore these aspects are not commented on further in this context.

[0512] The output of the Identifying Steps is included, i.e. accepted, time sequence windows wherein said time sequence windows 810 are included the best possible way. This means that these time sequence windows 810 contain single pressure waves related to cardiac beat-induced pressure waves, not to artifacts or a combination of artifacts and cardiac beat-induced pressure waves. Thereby the risk of computing false or misleading time sequence (TS.x)-related parameters is made minimal.

[0513] In the Computing Step, time sequence (TS.x)-related parameters 812 from included time sequences windows 810 are computed, said parameters selected from the group of:

• • [0514] mean value of starting diastolic minimum pressures of a time sequence window (TS.MeanP_min1)
  • [0515] standard deviation of mean value of starting diastolic minimum pressures of a time sequence window (TS.MeanP_min1_STD),
  • [0516] mean value of systolic maximum pressures of a time sequence window (TS.MeanP_max),
  • [0517] standard deviation of mean value of systolic maximum pressures of a time sequence window (TS.MeanP_max_STD),
  • [0518] mean amplitude of a time sequence window (TS.MeandP),
  • [0519] standard deviation of mean amplitude of a time sequence window (TS.MeandP_STD),
  • [0520] mean latency of a time sequence window (TS.MeandT),
  • [0521] standard deviation of mean latency of a time sequence window (TS.MeandT_STD),
  • [0522] mean rise time coefficient of a time sequence window (TS.MeanRT),
  • [0523] standard deviation of mean rise time coefficient of a time sequence window (TS.MeanRT_STD),
  • [0524] mean wave duration of a time sequence window (TS.MeanWD),
  • [0525] standard deviation of mean wave duration of a time sequence window (TS.MeanWD_STD),
  • [0526] mean single wave pressure of a time sequence window (TS.Mean_SWP),
  • [0527] standard deviation of mean single wave pressure of a time sequence window (TS.Mean_SWP_STD),
  • [0528] mean of diastolic minimum pressure differences of a time sequence window (TS.MeanDiff_P_min),
  • [0529] standard deviation of mean of diastolic minimum pressure differences of a time sequence window (TS.MeanDiff_P_min_STD),
  • [0530] mean of systolic maximum pressure differences of a time sequence window (TS.MeanDiff_P_max),
  • [0531] standard deviation of mean of systolic maximum pressure differences of a time sequence window (TS.MeanDiff_P_max—STD),
  • [0532] mean amplitude difference of a time sequence window (TS.MeanDiff_dP),
  • [0533] standard deviation of mean amplitude difference of a time sequence window (TS.MeanDiff_dP_STD),
  • [0534] mean latency difference of a time sequence window (TS.MeanDiff_dT),
  • [0535] standard deviation of mean latency difference of a time sequence window (TS.MeanDiff_dT_STD),
  • [0536] mean rise time coefficient difference of a time sequence window (TS.MeanDiff_RT),
  • [0537] standard deviation of mean rise time coefficient difference of a time sequence window (TS.MeanDiff_RT_STD),
  • [0538] mean wave duration difference of a time sequence window (TS.MeanDiff_WD),
  • [0539] standard deviation of mean wave duration difference of a time sequence window (TS.MeanDiff_WD_STD),
  • [0540] mean single wave pressure difference of a time sequence window (TS.MeanDiff_Mean_SWP),
  • [0541] standard deviation of mean single wave pressure difference of a time sequence window (TS.MeanDiff_Mean_SWP_STD),
  • [0542] number of accepted single pressure waves of a time sequence window (TS.SWCount),
  • [0543] mean wave amplitude of a time sequence window computed according to the first matrix (TS.MeanWavedP),
  • [0544] mean wave latency of a time sequence window computed according to the first matrix (TS.MeanWavedT),
  • [0545] mean wave rise time coefficient of a time sequence window computed according to the second matrix (TS.MeanWaveRT).

[0546] In particular, an analysis output is established selected from one or more of said time sequence (TS.x)-related parameters 812: Mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT), mean rise time coefficient (TS.MeanRT), and mean single wave pressure (TS.Mean_SWP). The computation of these time sequence (TS.x)-related parameters is now commented on.

[0547] The computation of mean wave amplitude (TS.MeanWavedP) and mean wave latency (TS.MeanWavedT) is now described. This analysis comprises the steps of creating a first matrix (Table 1) based on determining numbers of single pressure waves with pre-selected values related to amplitude (SW.dP) and latency (SW.dT), one axis of the matrix being related to an array of pre-selected values of pressure amplitude (SW.dP) and the other axis of the matrix being related to an array of pre-selected values of latencies (SW.dT), and indicating for each matrix cell at respective intersections in said first matrix a number of occurrence of matches between a specific pressure amplitude (SW.dP) and a specific latency (SW.dT) related to successive measurements of single pressure waves over said individual time sequence windows. The occurrence of matches in said matrix is indicated through actual number of matches during individual of said time sequence windows. The single pressure wave parameters of amplitude (SW.dP) and latency (SW.dT) are categorized into groups, said groups reflecting ranges of said single wave (SW.x)-related parameter values. The method comprises the further step of computing balanced position for a number of occurrences of said single pressure wave (SW.x)-related parameters amplitude (SW.dP) and latency (SW.dT) values during individual of said time sequence windows in said first matrix. The balanced position of said first matrix of numbers of amplitude (SW.dP) and latency (SW.dT) combinations corresponds to mean wave amplitude (TS.MeanWavedP) and mean wave latency (TS.MeanWavedT) during said individual time sequence windows.

[0548] A detailed example of computing the mean wave amplitude (TS.MeanWavedP) and mean wave latency (TS.MeanWavedT) is now given with reference to FIG. 4b. The mathematical process is intended to illustrate the concept, not to limit the scope of the invention. The process of determining the included single pressure waves 408 within the time sequence window 405 was described in connection with FIGS. 4a and 4b. The included, i.e. accepted, single pressure waves 408 (identified by included SW.P_max 410/SW.P_min1 411 pairs) during the time sequence window shown 405 shown in FIG. 4b (Time Sequence[360]) are termed SW[1], SW[2], SW[3], SW[4], and SW[5]. For each of these single pressure waves 408 both the amplitude (SW.dP) and latency (SW.dT) values were computed. The number of occurrences of single pressure waves 408 with certain amplitude (SW.dP) and latency (SW.dT) combinations are plotted into a first matrix that is two-dimensional. A part of such a matrix is presented in Table 1, illustrating the distribution of amplitude (SW.dP)/latency (SW.dT) combinations of the five included single pressure waves 408 (SW[1], SW[2], SW[3], SW[4], and SW[5]) presented in FIG. 4b. For example, single pressure waves 408 with amplitude (SW.dP) values greater or equal to 2.5 mmHg but less than 3.0 mmHg and latency (SW.dT) values greater or equal to 0.10 seconds, but less than 0.11 seconds occurred once during the time sequence window 405 of 6 seconds shown in said first matrix (Time Sequence[360]). The amplitude (SW.dP) values are presented in the columns and the latency (SW.dT) values in the rows. The matrix shown in Table 1 represents only a small fraction of a matrix of 1800 cells. A total of 60 amplitude (SW.dP) groups were created using a range of amplitudes (SW.dP) equal to 0 to 30.0 mmHg, with intervals of 0.5 mmHg, giving a total of 60 columns. A total of 30 latency (SW.dT) groups were created using a range of latencies (SW.dT) from 0.10 to 0.40 seconds with intervals 0.01 seconds, giving a total of 30 rows. For example, the first column corresponds to the first amplitude (SW.dP) group, named 0.5 (corresponding to 0.5 mmHg); this group includes amplitude (SW.dP) values greater or equal to 0.5 mmHg, but less than 1.0 mmHg (indicated by the group range 0.5≦SW.dP≦1). The midpoint (or mean) of the group is 0.75 [(0.5+1.0)/2]. Since the observation is categorized or grouped, the midpoint of the group is used. Similarly, the first latency (SW.dT) group is termed 0.1, corresponding to a latency of 0.1 seconds. This latency group includes latencies with duration greater or equal to 0.10 seconds, but less than 0.11 seconds (indicated by the group range 0.10≦SW.dT<0.11). The group midpoint is 0.105 [(0.10+0.11)/2]. The matrix distribution is computed during each individual of said time sequence windows 405 in a continuous series of said time sequence windows 405 during a recording. In this particular example duration of 6 seconds was used. The duration of each selectable time sequence window should lie in a time range of 5-15 seconds, though these durations represent no limitation of the scope of the invention.

[0549]

TABLE 1
A small part of a first matrix showing the single pressure wave distribution of Time Sequence[360] presented in FIG. 4b.
Group name		0.5	1	1.5	2	2.5	3	3.5
	Group		0.5 ≦	1.0 ≦	1.5 ≦	2.0 ≦	2.5 ≦	3.0 ≦	3.5 ≦ SW.dP <
	range		SW.dP <	SW.dP <	SW.dP <	SW.dP <	SW.dP <	SW.dP <	4.0
			1.0	1.5	2.0	2.5	3.0	3.5
		Group	0.75	1.25	1.75	2.25	2.75	3.25	3.75
		midpoint
0.1	0.10 ≦ SW.dT < 0.11	0.105					1	1
0.11	0.11 ≦ SW.dT < 0.12	0.115						1
0.12	0.12 ≦ SW.dT < 0.13	0.125
0.13	0.13 ≦ SW.dT < 0.14	0.135
0.14	0.14 ≦ SW.dT < 0.15	0.145
0.15	0.15 ≦ SW.dT < 0.16	0.155
0.16	0.16 ≦ SW.dT < 0.17	0.165
0.17	0.17 ≦ SW.dT < 0.18	0.175
0.18	0.18 ≦ SW.dT < 0.19	0.185
0.19	0.19 ≦ SW.dT < 0.20	0.195
0.2	0.20 ≦ SW.dT < 0.21	0.205
0.21	0.21 ≦ SW.dT < 0.22	0.215
0.22	0.22 ≦ SW.dT < 0.23	0.225
0.23	0.23 ≦ SW.dT < 0.24	0.235							1
0.24	0.24 ≦ SW.dT < 0.25	0.245
0.25	0.25 ≦ SW.dT < 0.26	0.255
0.26	0.26 ≦ SW.dT < 0.27	0.265						1
0.27	0.27 ≦ SW.dT < 0.28	0.275
0.28	0.28 ≦ SW.dT < 0.29	0.285

[0550] When computing the mean frequency of a two-dimensional distribution with i rows and j columns, both dimensions must be considered (c=columns; r=rows). The result is the mean value of the distribution when the values from both the row and column mean are considered. First, the latency (SW.dT) mean value (or row mean), with respect to the amplitude (SW.dP) values (columns) is determined (m_i). The m_i for each latency (ΔT) row is determined, by using the equation 77.

[0551] $\begin{array}{cc}{m}_i=\sum _{j=1}^c{A}_j{w}_{\mathrm{ij}}& \left(77\right)\end{array}$

[0552] A_j is the j^th column midpoint, referring to an amplitude (SW.dP) group value; and w_ij is the frequency (count) of the i^th SW.dT row and j^th SW.dP column cells.

[0553] $\begin{array}{cc}\mathrm{Row}\mathrm{mean}=\mathrm{Mean}\left(\mathrm{dt}\right)=\frac{\sum _{i=1}^r{m}_i{B}_i}{\sum _{i=1}^r\mathrm{mi}}& \left(78\right)\end{array}$

[0554] B_i is the i^th row SW.dT midpoint value (r=row). The term “i^th SW.dT row and j^th SW.dP column cell” refers to a matrix cell with the coordinates “i^th row and j^th column cell”. Such a cell is found in the crossing point of a horizontal line drawn through the midpoint of row i, and a vertical line drawn through the midpoint of column j. To illustrate the process, the data of Table I are used to calculate the mean row value. Application of the equations (77) and (78) gives a row mean with respect to columns equal to 0.169 seconds (2.746/16.25). The calculations are shown in more detail in Table 2.

[0555]

TABLE 2
Computation of row [latency (SW.dT)] mean with respect to columns
[amplitude (SW.dP)].
	mi	SW.dTi	mi × SW.dTi
	1 × 2.75 + 1 × 3.25 = 6.0	0.105	0.630
	1 × 3.25 = 3.25	0.115	0.374
	1 × 3.75 = 3.75	0.235	0.881
	1 × 3.25 = 3.25	0.265	0.861
	Sum = 16.25		2.746
	Row mean: 2.746/16.25 = 0.169 seconds

[0556] Second, the mean (SW.dP) value (columns), with respect to the latency (SW.dT) value (rows), is determined (m_j). The column amplitude (SW.dP) mean value are found using the same approach as used for finding the mean row latency (SW.dT) value. First, the m_j for each SW.dP column is found, as given in equation (79).

[0557] $\begin{array}{cc}{m}_j=\sum _{i=1}^r{B}_i{w}_{\mathrm{ij}}& \left(79\right)\end{array}$

[0558] B_i is the i^th row SW.dT midpoint, referring to a SW.dT group value and w_ij is the frequency for the i^th row and j^th column.

[0559] $\begin{array}{cc}\mathrm{Column}\mathrm{mean}=\mathrm{Mean}\left(\mathrm{dP}\right)=\frac{\sum _{j=1}^c{m}_j{A}_j}{\sum _{j=1}^c\mathrm{mi}}& \left(80\right)\end{array}$

[0560] A_j is the j^th column SW.dP value midpoint (c=column). The calculations are shown in Table 3, using the equations (79) and (80), the column mean with respect to rows will be equal to 3.329 mmHg (2.746/0.825).

[0561]

TABLE 3
Computation of column [amplitude (SW.dP)] mean with respect
to rows [latency (SW.dT)].
mj	SW.dPj	mj × SW.dPj
1 × 0.105 = 0.105	2.75	0.289
1 × 0.105 + 1 × 0.115 + 1 × 0.265 = 0.485	3.25	1.576
1 × 0.235 = 0.235	3.75	0.881
Sum = 0.825		2.746
Column mean: 2.746/0.825 = 3.329 mmHg

[0562] Thus, the mean wave for the particular time sequence 405 shown in FIG. 4b (Time Sequence[360]) was 0.17 seconds/3.33 mmHg, the mean intracranial pressure (ICP) wave of this time sequence window 405 had a mean wave latency (TS.MeanWavedT) of 0.17 seconds (TS[360].MeanWavedT=0.17 sec) and an mean wave amplitude (TS.MeanWavedP) of 3.33 mmHg (TS[360].MeanWavedT=3.33 mmHg).

[0563] It is now described the process of computing mean wave rise time coefficient (TS.MeanWaveRT). This analysis comprises the steps of creating a second matrix that is one-dimensional, based on determining number of single pressure waves with pre-selected values related to rise time coefficient (SW.RT), the axis being related to an array of pre-selected values of rise time coefficient (SW.RT), and wherein indicating for each matrix cell in said second one-dimensional matrix a number of occurrences of pre-selected rise time coefficients (SW.RT) related to successive measurements of single pressure waves during said individual time sequence window. The single pressure wave parameter rise time coefficient (SW.RT) is categorized into groups, said groups reflecting ranges of said single wave (SW.x)-related parameter values. It is computed balanced position for a number of occurrences of said single pressure wave (SW.x)-related parameter rise time coefficient (SW.RT) in said second matrix, to yield an analysis output. Said balanced position of said second matrix of numbers of rise time coefficient (SW.RT) combinations corresponds to the mean wave rise time coefficient (TS.MeanWaveRT) of said time sequence window. TS.MeanWaveRT is computed according to equation (81). There are two variables x_i, and w_i where x_i is equal to the Group midpoint of each observed SW.RT and w_i is equal to the frequency or number of occurrences within a Group range. The Observation value x_i is the group midpoint of the observed SW.RT.

[0564] $\begin{array}{cc}\stackrel{\_}{X}=\frac{\sum _{i=1}^k{x}_i{w}_i}{\sum _{i=1}^k{w}_i},k=\mathrm{number}\mathrm{of}\mathrm{observations}& \left(81\right)\end{array}$

[0565] An example of a second matrix is now shown in Table 4 (the observations are example values for the purpose of illustrating the concept).

[0566]

TABLE 4
A second matrix showing a single pressure wave distribution.
Group name	4.5	5.0	5.5	6.0
Group range	4.5 ≦ SW.dP < 5.0	5.0 ≦ SW.dP < 5.5	5.5 ≦ SW.dP < 6.0	6.0 ≦ SW.dP < 6.5
Group midpoint	4.75	5.25	5.75	6.25
Observation	2	1	4

[0567] According to equation (81), balanced position of single pressure wave distribution of rise time coefficient in Table 4 equals:
(2×4.75 mmHg/sec+1×5.25 mHg/sec+4×5.75 mmHg/sec)/7=5.39 mmHg/sec.

[0568] Mean amplitude pressure of a time sequence window (TS.MeandP) corresponds to the sum of amplitude (SW.dP) values divided by the number of individual single pressure waves during said individual time sequence window. The mathematical process is further described in equation (21).

[0569] Mean latency of a time sequence window (TS.MeandT) corresponds to the sum of latency (SW.dT) values divided by number of individual single pressure waves during said individual time sequence window. The mathematical process is further described in equation (23).

[0570] Mean rise time coefficient of a time sequence window (TS.MeanRT) corresponds to the sum of rise time coefficient (SW.RT) values divided by the number of individual single pressure waves during said individual time sequence window. The mathematical process is further described in equation (25).

[0571] Absolute mean single wave pressure of a time sequence window (TS.Mean_SWP) corresponds to the sum of mean pressure (SW.Mean_SWP) values divided by number of individual single pressure waves during said individual time sequence window. Mean pressure value for an individual of said single pressure waves (SW.Mean_SWP) is the sum of sample values during the time of a wave duration, i.e. from starting diastolic minimum pressure (SW.P_min1) to ending diastolic minimum pressure (SW.P_min2)-1 divided by number of samples. For example, mean single wave pressure (TS.Mean_SWP) for the time sequence window 405 shown in FIG. 4b is the average of mean pressure for each of the five included single pressure waves 408 (termed SW[1], SW[2], SW[3], SW[4], and SW[5]), as further detailed in equation (29). Mean pressure for each included single pressure wave (SW.Mean_SWP) 408 is computed according to equation (9). Mean single wave pressure (TS.Mean_SWP) for the time sequence window 405 is the average of mean pressure of the five accepted single pressure waves [TS[360]. Mean_SWP=(11.87+11.89+11.65+11.56+12.36)/5=11.87 mmHg]. Mean pressure was 11.87 mmHg for SW[1] (913.99 mmHg/77 samples), 11.89 for SW[2] (1034.4 mmHg/87 samples), 11.65 mmHg for SW[3] (1036.85 mmHg/89 samples), 11.56 mmHg for SW[4] (971 mmHg/84 samples), and 12.36 for SW[5] (1087.68/88 samples).

[0572] Reference is now again given to FIG. 8. During said Computing Step the time sequence (TS.x)-related parameters 812 are determined on the basis of included time sequence windows 810, which were determined through the Identifying Steps. Based on the determined time sequence (TS.x)-related parameters 812, an analysis output 813 is determined in said Presentation Step (FIG. 8). Said analysis output 813 can be presented in different ways, for example including one or more of said parameters: Mean wave amplitude (TS.MeanWavedP), mean wave latency (TS.MeanWavedT), mean wave rise time coefficient (TS.MeanWaveRT), mean amplitude (TS.MeandP), mean latency (TS.MeandT), mean rise time coefficient (TS.MeanRT), and mean single wave pressure (TS.Mean_SWP).

[0573] During an ongoing sampling and digitalization of pressure-related signals 801, said analysis output 813 can be presented as numerical values of time sequence (TS.x)-related parameters 812 on a display 814 for each of said included time sequence windows 810. Given duration of said individual time sequence windows 810 of 6 seconds, the parameter value is displayed each 6 seconds on said display 814. For example, mean wave amplitude (TS.MeanWavedP) and mean wave latency (TS.MeanWavedT) can be displayed as the values 6.5 mmHg/0.23 seconds during Time Sequence[n] and as e.g. values 6.7 mmHg/0.24 seconds during Time Sequence[n+1] (the values 6.5 mmHg/0.23 seconds and 6.7 mmHg/0.24 seconds are only example values). Thereby, the parameter values are updated each 6 seconds, given this particular time window duration.

[0574] Another way of presenting the analysis output 813 of one or more of said time sequence parameters 812 is creation of histogram 815 distribution of values of said parameters 812. Typically such a histogram 815 includes a selectable number of time sequence windows of a signal 801. During an ongoing sampling and digitalization of pressure-related signals 801, said histogram 815 may include all included time sequence windows 810 that have been included so far. Given that e.g. 700 time sequence windows 810 have been included (i.e. Time Sequence[1] to Time Sequence[700]), the histogram 815 distribution is created based on time sequence (TS.x)-related parameters 812 of 700 included time sequence windows 810. Given that histogram 815 is created after the end of sampling of said pressure-related signals 801, the histogram 815 distribution is typically created based on time sequence (TS.x)-related parameters 812 of the included time sequence windows 810 of said signal 801.

[0575] Still another way of presenting the analysis output 813 of one or more of said time sequence parameters 812 is creation of a quantitative matrix 816 of values of said parameters 812. Typically such a quantitative matrix 816 includes a selectable number of time sequence windows of a signal 801. Said quantitative matrix 816 is created based on determining numbers of one of said time sequence parameters 812 with pre-selected values of said parameters 812, wherein one axis of the quantitative matrix 816 is related to an array of pre-selected values of said parameter 812, wherein the other axis is related to an array of pre-selected numbers of consecutive included time sequence windows 810, and wherein indicating for each matrix cell at respective intersections in said quantitative matrix 816 a number of occurrence of matches between a specific parameter 812 value and a specific number of included time sequence windows 810. The parameter 812 values are categorized into groups, said groups reflecting ranges of said parameter 812 values. Furthermore, the occurrence of matches in said quantitative matrix 816 is indicated through actual number or standardisation based number of matches during a specific measurement period, said standardisation based number of matches being a function of the length of the specific measurement period. A measurement period refers to given duration of said signal 801. A pressure signal 801 refers to a number of sequential and available pressure-related samples during a time period, wherein each of said time-related sequential samples can be referenced by a sample number and elapsed time determined by sample location number and sample frequency.

[0576] To further illustrate the creation of a quantitative matrix 816, a specific example is now presented, though this example is not intended to limit the scope of the invention.

[0577] Given a continuous intracranial pressure (ICP) signal 801 of 6 hours duration including a total of 3000 included, i.e. accepted, time sequence windows 810, there is allowed computation of time sequence (TS.x)-related parameters 812 of said 3000 included time sequence windows 810 (Time Sequence[1] to Time Sequence[3000]). In this example it is focused on mean wave amplitude (TS.MeanWavedP). The quantitative matrix shown in Table 5 is based on all the 3000 included time sequence windows 810 and corresponding 3000 (TS.MeanWavedP) parameter values. As indicated in Table 5, said quantitative matrix is created based on determining numbers of parameters 812 with pre-selected values of TS.MeanWavedP 812. The one axis of the quantitative matrix is related to an array of pre-selected values of TS.MeanWavedP 812 (in this example the pre-selected values were 4.0 mmHg, 5.0 mmHg, and 6.0 mmHg, referred to as group ranges). The parameter 812 values are categorized into groups, said groups reflecting ranges of said parameter 812 values: Amplitude Group 4: 4.0 mmHg≦TS.MeanWavedP<5.0 mmHg. Amplitude Group 5: 5.0 mmHg≦TS.MeanWavedP<6.0 mmHg. Amplitude Group 6: 6.0 mmHg≦TS.MeanWavedP<7.0 mmHg. The other axis is related to an array of pre-selected numbers of included time sequence windows 810 (in this example the pre-selected numbers were 5, 10 and 20). Since the duration of each of said individual time sequence windows is known (6 seconds in this example), this other axis also is related to pre-selected durations (in this example 30 seconds, 60 seconds, and 120 seconds): Time Sequence Number Group 5: 0<N≦5 (corresponding to Time Group 0<Time≦30). Time Sequence Number Group 10: 5<N≦10 (corresponding to Time Group 30<Time≦60). Time Sequence Group 20: 10<N≦20 (corresponding to Time Group 60<Time≦120). For each matrix cell is indicated at respective intersections in said quantitative matrix a number of occurrence of matches between a specific parameter (TS.MeanWavedP) 812 value and a specific number of included time sequence windows 810. The occurrence of matches in this matrix (Table 5) is indicated through actual number of matches during the specific measurement period of 6 hours. For example, according to this matrix distribution, mean wave amplitude (TS.MeanWavedP) values 812 equal to or larger than 5.0 mmHg but less than 6.0 mmHg occurring in five up to ten consecutive included time sequence windows 810 (5<N≦10) occurred two times during the recording of 6 hours including a total of 3000 included time sequence windows 810. In Table 6 is presented an identical quantitative matrix as in Table 5, though in this quantitative matrix (Table 6) the occurrence of matches is indicated through standardisation-based number of matches being a function of the length of the specific measurement period. The occurrence was standardised to a length of a recording period of 1 hour. Thereby each number is said matrix cell was multiplied with a factor equal to ⅙ in this specific example.

[0578]

TABLE 5
A quantitative matrix of pre-selected combinations of TS.MeanWavedP
812 parameter values and number of included time sequence windows 810 (actual
numbers presented in each matrix cell).
Group name		4	5	6
	Group ranges	4.0 ≦ DP < 5.0	5.0 ≦ DP < 6.0	6.0 ≦ DP < 7.0
5	0 < N ≦ 5	0 < Time ≦ 30	8	5	2
10	5 < N ≦ 10	30 < Time ≦ 60	6	2	1
20	10 < N ≦ 20	60 < Time ≦ 120	3	1
DP refers to TS.Mean WavedP group range;
N refers to number of included time sequence windows; and
Time refers to the time of corresponding time sequences windows.

[0579] According to this quantitative matrix (Table 5), within said signal containing 3000 included time sequence windows there were five occurrences for which up to five consecutive time sequence windows had a time sequence parameter TS.MeanWavedP between 5.0 and up to 6.0 mmHg (5.0≦DP<6.0; the number 5 is highlighted in bold type; Table 5).

[0580]

TABLE 6
A quantitative matrix of pre-selected combinations of TS.MeanWavedP
812 parameter values and number of included time sequence windows 810
(standardization based numbers presented in each matrix cell).
Group name		4	5	6
	Group ranges	4.0 ≦ DP < 5.0	5.0 ≦ DP < 6.0	6.0 ≦ DP < 7.0
5	0 < N ≦ 5	0 < Time ≦ 30	1.3 (=8 × 1/6)	0.8 (=5 × 1/6)	0.3 (=2 × 1/6)
10	5 < N ≦ 10	30 < Time ≦ 60	1 (=6 × 1/6)	0.3 (=2 × 1/6)	0.2 (=1 × 1/6)
20	10 < N ≦ 20	60 < Time ≦ 120	0.5 (=3 × 1/6)	0.2 (=1 × 1/6)
DP refers to TS.Mean WavedP group range;
N refers to number of included time sequence windows; and
Time refers to the time of corresponding time sequences windows.

[0581] In test recordings the quantitative matrix has been shown to be of great clinical significance, as related to several causes. It may be somewhat difficult to assess a continuous pressure recording of several hours duration since time sequence (TS.x)-related parameters may sometimes change over time. Said quantitative matrix may be used to get a useful summary of the recording. In particular, said quantitative matrix is useful for comparisons of continuous pressure recordings between different individuals. Test recordings including computation of quantitative matrixes of TS.MeanWavedP and TS.MeandP have been found to be of great value. The opportunity to standardize the quantitative matrix is particularly useful for comparisons of pressure recordings between individuals. Thereby, this inventive aspect further enhance the diagnostic value of pressure monitoring,

[0582] Finally, the analysis output 813 of one or more of said time sequence parameters 812 may be presented in various other ways though specific examples are not presented in this context.

[0583] Concerning the method for processing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity as summarized in FIG. 8, some examples are given concerning the usefulness of the method.

[0584] For continuous intracranial pressure (ICP) signals 801, the method of computing time sequence (TS.x)-related parameters 812 such as mean wave amplitude (TS.MeanWavedP), mean amplitude (TS.MeandP), mean wave latency (TS.MeanWavedT), and mean latency (TS.MeandT) is useful for determining intracranial compliance (i.e. the inverse of intracranial elastanse). Determination of intracranial compliance is one of the major reasons for intracranial pressure (ICP) monitoring, though existing and currently used methods in no reliable way reveal intracranial compliance. Thus, the inventive method provides information within the pressure signal 801 that is not revealed by existing and currently used methods.

[0585] Using currently used, prior art technology of pressure monitoring, wrong calibration against atmospheric zero pressure level may give misleading results. Drift of zero pressure level is another great problem related to existing and currently used pressure monitoring. During long-term pressure monitoring, drift of zero pressure level may give misleading pressure measurements. Since the time sequence (TS.x)-related parameters 812 are relative values, errors related to wrong zero pressure level or drift in zero pressure level is eliminated by this invention.

[0586] By existing and currently used pressure monitoring technology, there are limited ways of controlling the quality of the pressure-related signals. A misleading pressure measurement may be made given wrong placement of the pressure sensor. There is minimal opportunity to control whether the pressure measurements are related to the cardiac beat induced pressures. By this invention the time sequence (TS.x)-related parameters 812 are only computed for included time sequence windows 810, including included single pressure waves 807. This approach gives an opportunity to control the quality of the pressure measurements.

[0587] Reference is now given to the third feature of this invention. Two different aspects of said third feature are described. According to a first aspect, a method is described for determining relationships between time sequence (TS.x)-related parameters of identical time sequence windows within two or more simultaneous signals. Thereby a reference material is created for adjustment (or calibration) of individual non-invasive signals, as further explained in a second aspect of said third feature. According to said another aspect, a method is described for adjustment of individual pressure-related signals using said relationships between time sequence (TS.x)-related parameters of identical time sequence windows within two or more simultaneous signals. This third feature of the invention is particularly illustrated in FIG. 9, as well as in FIGS. 7, 10 and 11.

[0588] Said first aspect of said third feature of the invention relates to a method for processing two or more simultaneous continuous pressure-related signals derivable from a human or animal body from one or more locations thereof electable from: inside the body, outside the body, inside body cavity, outside body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with identical time reference, wherein for selectable and simultaneous time sequence windows the method comprises the further steps of identifying from said digital data signal single pressure waves related to cardiac beat-induced pressure waves within said two or more simultaneous signals constituting a pressure recording, and identifying from said digital data signal pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves within said two or more simultaneous signals constituting a pressure recording, and computing time sequence (TS.x)-related parameters of said single pressure waves during said identical time sequence windows within said two or more simultaneous signals constituting a pressure recording. The method comprises the further steps of determining relationships between time sequence (TS.x)-related parameters of said identical time sequence windows within said two or more simultaneous signals constituting a pressure recording, said relationships calculated as related time sequence (rTS.x) parameters, and determining said related time sequence (rTS.x) parameters for individual recordings or a population of recordings. Said related time sequence (rTS.x) parameters are further used for formula-based adjustment of time sequence windows of individual pressure-related signals, and for creating factorized time sequence (rTS.x) parameters of said individual time sequence windows of said individual continuous pressure-related signal.

[0589] Said second aspect of said third feature of the invention relates to a method for processing continuous pressure-related signals derivable from locations inside or outside a human or animal body or body cavity, comprising the steps of obtaining samples of said signals at specific intervals, and converting thus sampled pressure signals into pressure-related digital data with a time reference, wherein for selectable time sequence windows the method comprising the further steps of identifying from said digital data single pressure waves related to cardiac beat-induced pressure waves, and identifying from said digital data pressure waves related to artifacts or a combination of artifacts and cardiac beat-induced pressure waves and computing time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows. The method comprises the further steps of:

[0590] adjusting said time sequence (TS.x)-related parameters of said single pressure waves during individual of said time sequence windows using formula-based adjustments based on related time sequence (rTS.x) parameters, creating from said formula-based adjustments factorized time sequence (fTS.x) parameters of said individual time sequence windows of said continuous pressure-related signals, and establishing an analysis output selected from one or more of said factorized time sequence (fTS.x)-related parameters of said single pressure waves during individual of said time sequence windows. Hence, this second aspect of said third feature of the invention can relate to adjusting one non-invasive signal using formula-based adjustments from a historical source; the method does not depend on two simultaneous signals.

[0591] Some remarks are required concerning simultaneously sampled pressure-related signals. With reference to FIG. 1, the notation:

[0592] “Recording[1].Signal[m].Type.Location” denotes a specific Location 113 within a specific Type 104 within a specific Signal [n] 102, within a specific Recording [1] 101. The notation “Recording [1].Signal [m].Type.Sensor” denotes a specific Sensor 112 within a specific Type 104 within a specific Signal [n] 102, within a specific Recording [1] 101.

[0593] The present invention relates to a method for processing most types of continuous pressure-related signals derivable from human beings or animals, independent of pressure measurement locations and/or sensor type, and whether said sensor is placed inside or outside a human or animal body or a body cavity. Said method for processing continuous pressure-related signals is also independent of starting points of said continuous pressure-related signals. Furthermore, said method for processing continuous pressure-related signals is independent on type of pressure sensor, said sensor being placed inside or outside a human or animal body and/or body cavity.

[0594] Reference is now given to FIG. 9, wherein an overview of said method for analyzing two or more simultaneous continuous pressure-related signals is shown. From a recording 901 (Recording[1]) is analyzed two simultaneous signals, Signal[1] 902 and Signal[2] 903. Samples are obtained from each respective one of said pressure related signals Signal[1] 902 and Signal[2] 903, each such sample containing a pressure value at a specific time, and wherein said two or more pressure-related signals 902, 903 are all sampled simultaneously. Each of said sampled pressure signals 902, 903 refers to a number of sequential and available pressure samples during a time period. Selectable and simultaneous time sequence windows are a selected time frame of a sampled signal. Each of such simultaneous selectable time sequence windows is a function of a number of time-related sequential samples, each individual sample referenced by a sample number and elapsed time determined by sample location number and sample frequency. Concerning notation related to recording 901 and signals 902 and 903, it is referred to FIG. 1. In short, a recording 901 is one or more simultaneous signals (e.g. Signal[1] 902 and Signal[2] 903) derivable from locations inside or outside a human or animal body or body cavity, each of said signals having identical time reference, though it is not a requirement that the start time is identical for all signals of a recording. Attributes of each signal include type, frequency, and actual samples. As indicated with reference to FIG. 1, a signal is equivalent to: Recording [1].Signal [m].Samples [n], wherein each of said samples contains a pressure value at a specific time.

[0595] As indicated in FIG. 9, the process method incorporates different Identifying Steps, Computing Step, Determining and Application Step. Said Identifying Steps include identification of all separate peaks and valleys 904, 905 in said simultaneously sampled signals (Signal[1] 902 and Signal[2] 903) that constitute a pressure recording (Recording[1] 901). Each of said peaks is a sample with a pressure value and a time stamp or location, and each of said valleys is a sample with a pressure value and a time stamp or location.

[0596] The result of applying signal specific General Methods Criteria 906, 907 is either included peak/valley pair combinations 908, 909 or excluded peak/valley pair combinations 910, 911 in said simultaneous signals (Signal[1] 902, Signal[2] 903) constituting a pressure recording (Recording[1] 901).

[0597] After applying signal specific Single Wave & Delta Single Wave Criteria 912, 913 to said included peak/valley pairs 908, 909, the output is either included single pressure waves 914, 915 or excluded pressure waves 916, 917 in said simultaneous signals (Signal[1] 902, Signal[2] 903), constituting a pressure recording (Recording[1] 901). Said criteria 912, 913 relate to thresholds and ranges of single pressure wave (SW.x)-related parameters and delta single pressure wave (ΔSW.x)-related parameters during time sequence windows. After applying the Single Wave & Delta Single Wave Criteria 912, 913 to included pair combinations of peak/valley pairs 908, 909 in said simultaneous signals (Signal[1] 902, Signal[2] 903), said pairs 908, 909 correspond to included single pressure waves 914, 915 in said simultaneous signals (Signal[1] 902, Signal[2] 903) constituting a pressure recording (Recording[1] 901). Pair combinations of diastolic minimum pressure (SW.P_min1) and systolic maximum pressure (SW.P_max) characterize single pressure waves created by cardiac beat-induced pressure waves. Said criteria 912, 913 exclude for further analysis pressure waves 916, 917 (i.e. minimum-maximum pressure (SW.P_min1/SW.P_max) pairs) during said time sequence windows with said single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters outside selected criteria 912, 913 for thresholds and ranges of said parameters. Said criteria 912, 913 include for further analysis single pressure waves 914, 915 having single pressure wave (SW.x)-and delta single pressure wave (ΔSW.x)-related parameters within selected criteria 912, 913 for thresholds and ranges of said single pressure wave (SW.x)-related parameters. Pair combinations of diastolic minimum pressure (SW.P_min1) and systolic maximum pressure (SW.P_max) correspond to the diastolic minimum pressures and systolic maximum pressures of individual of pressure waves created by each of said cardiac beats.

[0598] Signal-specific Time Sequence & Delta Time Sequence Criteria 918, 919 are applied to each of said time sequence windows in a continuous series of time sequence windows of said simultaneous signals (Signal[1] 902, Signal[2] 903) constituting a pressure recording (Recording[1] 901). Said criteria 918, 919 for thresholds and ranges of said time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters determine included time sequence windows 920, 921 and excluded time sequence windows 922, 923 of said simultaneous signals (Signal[1] 902, Signal[2] 903) constituting a pressure recording (Recording[1] 901). Said criteria 918, 919 exclude for further analysis time sequence windows 922, 923 with time sequence (TS.x)-and delta time sequence (ΔTS.x)-related parameters outside selected criteria 918, 919 for thresholds and ranges of said parameters. Said criteria 918, 919 include for further analysis time sequence windows 920, 921 having time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters within selected criteria 918, 919 for thresholds and ranges of said time sequence (TS.x) and delta time sequence (ΔTS.x)-related parameters.

[0599] It should be understood that said General Methods Criteria 906, 907, Single Wave & Delta Single Wave Criteria 912, 913 and Time Sequence & Delta Time Sequence Criteria 918, 919 are signal-specific and location-specific, meaning that said criteria are different for different types of signals and locations in said signals. In a next Computing Step (FIG. 9) is computed time sequence (TS.x)-related parameters 924, 925 for each individual of said included time sequence windows 920, 921 of said simultaneous signals (Signal[1] 902, Signal[2] 903) constituting a pressure recording (Recording[1] 901).

[0600] The Identifying Steps are applied to each of said time sequence windows in a continuous series of said time sequence windows during a recording.

[0601] The Computing Step is applied to each of said included time sequence windows 920, 921 in a continuous series of said time sequence windows during a recording.

[0602] In a final Determining Step, there is determined relationships between time sequence (TS.x)-related parameters 924, 925 of said identical included time sequence windows 920, 921 within said simultaneous signals (Signal[1] 902, Signal[2]903) constituting a pressure recording (Recording[1] 901). Said relationships are calculated as related time sequence (rTS.x) parameters 926, determined for individual recordings, as well as for a population of recordings. Said related time sequence (rTS.x) parameters 926 can be constant relationships and/or formula-based relationships between identical time sequence (TS.x)-related parameters 924, 925 of different pressure signals (Signal[1] 902, Signal[2] 903) with identical time reference. Said related time sequence (rTS.x) parameters are computed for each individual of said included time sequence windows 920, 921 in a continuous series of time sequence windows of said signals (Signal[1] 902, Signal[2] 903). In addition, for all included time sequence windows 920, 921 in said signals (Signal[1] 902, Signal[2] 903), the mean value of such parameters 926 is determined. For a population of recordings, population-based formulas for said related time sequence (rTS.x)-related parameters 926 are determined. Based on said related time sequence (rTS.x) parameters 926, formula-based adjustments of time sequences of individual pressure-related signals 927 are made possible. Thereby, factorized time sequence (fTS.x) parameters of individual time sequence windows of individual continuous pressure-related signal are created from said formula-based adjustments 927. Factorized time sequence (fTS.x) parameters are derived from related time sequence (rTS.x) 926 values together with time sequences of the signal being factorized.

[0603] For the sake of clarity, to the process of determining related time sequence (rTS.x) parameters 926 to be used for formula-based adjustments of individual signals 927 requires two or more simultaneous pressure-related signals. The particular implementation referred to as said second aspect of said third feature of the invention requires that related time sequence parameters 926 already have been determined for specific signals and locations. Such a historical material of related time sequence parameters 926 can then be used for formula-based adjustments of new individual pressure-related signals 927. For example for one individual non-invasive pressure-related signal factorized time sequence (fTS.x) parameters can be calculated from said historical material of related time sequence parameters 926.

[0604] Concerning the Identifying Steps, the method is comparable to the process described for FIGS. 2, 3a, 3b, 3c, 4a, 4b, 5a, 5b, 6, and 7a, but with reference to FIG. 9 the method is applied to several simultaneous signals.

[0605] Details about General Methods Criteria 906, 907, Single Wave & Delta Wave Criteria 912, 913 and Time Sequence & Delta Time Sequence Criteria 918, 919 are already commented on with reference to FIGS. 2 and 8, and these aspects are therefore not commented on further in the context of FIG. 9.

[0606] With reference to FIG. 9 it is made clear that said criteria are signal-specific and location specific being different for different signal types and locations of a signal.

[0607] Computation of related time sequence (rTS.x) parameters can be selected from the group of

• • [0608] relationship of mean values of starting diastolic minimum pressure of two or more perfect time sequence windows from two or more different pressure signals (rTS.P_min1),
  • [0609] relationship of standard deviation of mean values of starting diastolic minimum pressure of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanP_min1—STD),
  • [0610] relationship of mean values of systolic maximum pressure of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanP_max),
  • [0611] relationship of standard deviation of mean values of systolic maximum pressure of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanP_maxSTD),
  • [0612] relationship of mean amplitude values of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeandP),
  • [0613] relationship of standard deviation of mean amplitude of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeandP_STD),
  • [0614] relationship of mean latency of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeandT),
  • [0615] relationship of standard deviation of mean latency of two or more perfect time sequence windows from two or more two different pressure signals (rTS.MeandT_STD),
  • [0616] relationship of mean rise time coefficient of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanRT),
  • [0617] relationship of standard deviation of mean rise time coefficient of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanRT_STD),
  • [0618] relationship of mean wave duration of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanWD),
  • [0619] relationship of standard deviation of mean wave duration of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanWD_STD),
  • [0620] relationship of mean single wave pressure of two or more perfect time sequence windows from two or more different pressure signals (rTS.Mean_SWP),
  • [0621] relationship of standard deviation of mean single wave pressure of two or more perfect time sequence windows from two or more different pressure signals (rTS.Mean_SWP_STD),
  • [0622] relationship of mean diastolic minimum pressure difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_P_min),
  • [0623] relationship of standard deviation of mean diastolic minimum pressure difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_P_min—STD),
  • [0624] relationship of mean systolic maximum pressure difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_P_max),
  • [0625] relationship of standard deviation of mean systolic maximum pressure difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_P_max—STD),
  • [0626] relationship of mean amplitude difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_dP),
  • [0627] relationship of standard deviation of mean amplitude difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_dP_STD),
  • [0628] relationship of mean latency difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_dT),
  • [0629] relationship of standard deviation of mean latency difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_dT_STD),
  • [0630] relationship of mean rise time coefficient difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_RT),
  • [0631] relationship of standard deviation of mean rise time coefficient difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_RT_STD),
  • [0632] relationship of mean wave duration difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_WD),
  • [0633] relationship of standard deviation of mean wave duration difference of two or more perfect time sequence windows from two or more two different pressure signals (rTS.MeanDiff_WD_STD),
  • [0634] relationship of mean single wave pressure difference of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanDiff_Mean_SWP),
  • [0635] relationship of standard deviation of mean single wave pressure difference of two or more perfect time sequence windows from two or more two different pressure signals (rTS.MeanDiff_Mean_SWP_STD),
  • [0636] relationship of single wave count of two or more perfect time sequence windows from two or more different pressure signals (rTS.SWCount),
  • [0637] relationship of mean wave amplitude of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanWavedP),
  • [0638] relationship of mean wave latency of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanWavedT), and
  • [0639] relationship of mean wave rise time coefficient of two or more perfect time sequence windows from two or more different pressure signals (rTS.MeanWaveRT).

[0640] The term “perfect” in this context implies the term “accepted” after application of very strict criteria.

[0641] It should be noted that said simultaneous continuous pressure-related signals (Signal[1] and Signal[2]) include two dimensions, a pressure scale and a time scale. The related time sequence (rTS.x) parameters which describe the relationships between two signals are based on observations between identical time sequences from said signals (Signal[1].TS[x] and Signal[2].TS[x]), where x denotes a specific location and time within the same recording (Recording[1]). Thus, the related time sequence (rTS.x) parameters establish relationships for both the pressure and time dimensions of a continuous signal.

[0642] In test recordings, related time sequence (rTS.x) parameters found useful for comparing pressure-related characteristics between simultaneous signals include such as, e.g.: rTS.MeandP, rTS.MeandP_STD, rTS.MeanRT, and rTS.MeanWavedP. Parameters found useful for comparisons of time related characteristics include such as, e.g.: rTS.MeandT, rTS.MeanWD, rTS.MeanWD_STD, rTS.MeanDiff_dT, and rTS.MeanDiff_WD. Comparisons of rTS.x values between different time sequence parameters may be even more powerful in determining relationships.

[0643] In general, it should be noted that the sensor element itself wherefrom the pressure signals are derived, could be located both within and outside the body cavity. The type of sensor does not represent a limitation of the scope of the present invention. For example, intra-dural pressure signals may be derived from a fluid catheter placed via a cannula within the intra-dural compartment, wherein the sensor element can be placed outside the body thus deriving pressure signals from the catheter fluid outside the body and in distance from the location per se (i.e. the intra-dural compartment). In another situation, a sensor element itself is located on the catheter tip that is placed within the intra-dural compartment. The situation is similar for arterial blood pressure (ABP) monitoring, when a catheter is placed via an intra-arterial cannula, and the sensor element is either outside the body measuring pressures within the distal part of the catheter or the sensor element is placed on the proximal tip of the catheter placed within the intra-arterial compartment. Therefore, the sensor type and sensor location represents no limitation of the scope of the invention.

[0644] Continuous simultaneous signals within a recording may be derivable from a human or animal body from one or more locations thereof electable from: inside the body, outside the body, inside body cavity, outside body cavity. Reference is now given to FIG. 9 showing two different signals (Signal[1]902, Signal[2] 903), and examples are now given concerning simultaneous pressure-related signals from different locations or pressure sources. Said simultaneously sampled signals can be obtained from two simultaneous continuous intracranial intra-dural and epidural pressure-related signals. In this situation the simultaneous signals constituting a recording are derived simultaneously from two different locations: Inside the dura mater (i.e. intra-dural) and outside the dura mater (i.e. epidural). For example, the corresponding notations can be: Recording[60].Signal[1].ICP.Intra-dural; and Recording[60].Signal[2].ICP.Epidural. These different extra- and intra-dural compartments can in a way be considered as different body cavities though both the extra- and intra-dural body cavities are located within the intracranial compartment and are measuring from the same pressure origin.

[0645] Continuous signals can be obtained from at least two simultaneous continuous intracranial intradural and intraspinal intra-dural cerebrospinal fluid pressure signals. For example, said intradural pressure-related signals may be derived from a catheter placed within the cerebral ventricles, or a sensor placed within the brain parenchyma or within the subdural extra-cerebral compartment. The sensor element may be within the intra-dural compartment or outside the body measuring fluid pressure within said catheter. Said intraspinal intra-dural cerebrospinal fluid pressure signals may as well be derived from a fluid-filled catheter with the sensor on the outside of the body or via a sensor element introduced to the intra-spinal and intra-dural compartment. Intraspinal intra-dural cerebrospinal fluid pressure signals are obtained during so-called infusion tests.

[0646] In another situation, said sampled signals are obtained from at least two simultaneous continuous intracranial intra-dural and extra-cranial pressure signals indicative of intracranial pressure signals. Said extra-cranial pressure signals indicative of intracranial pressure signals can be derived from various sources. Some examples are given though these examples are not intended to limit the scope of the invention:

• [0647] a) Firstly, said extra-cranial pressure signals can be related to air pressure signals derivable from within a human or animal outlet-sealed outer ear channel. An open and air-filled catheter is placed within the outer ear channel after airtight closing of the outer ear channel, thus allowing sampling of pressure-related signals from the outer airtight outer ear channel. A detailed description of this concept is given separately.
• [0648] b) Secondly, said extra-cranial pressure signals can also be transcranial Doppler signals, that are transformable into pressure-related signals indicative of intracranial pressure signals. Transcranial Doppler measures arterial blood flow signals that are transformable into pressure-related signals indicative of intracranial pressure signals.
• [0649] c) Thirdly, said extra-cranial pressure signals can be cranial impedance-related signals, being transformable into pressure-related signals indicative of intracranial pressure signals.
• [0650] d) Fourthly, said extra-cranial pressure signals are fontanel applanation pressure signals, being transformable into pressure-related signals indicative of intracranial pressure signals. This strategy is particularly useful in children below 1-2 years of age with a non-closed fontanel.
• [0651] e) Fifthly, said extra-cranial pressure signals are ocular applanation pressure signals being transformable into pressure-related signals indicative of intra-ocular pressure signals.

[0652] Said simultaneous pressure samples derived from intracranial intra-dural and extra-cranial pressure signals indicative of intracranial pressure signals may as well be combined with outer simultaneous signals constituting a pressure recording. Such signal may be arterial blood pressure (ABP) signals or electrocardiogram (ECG) signals.

[0653] Simultaneous pressure-related signals may be sampled from at least two simultaneous continuous intra-arterial and extra-arterial pressure signals indicative of intra-arterial pressure signals. Said extra-arterial pressure signals indicative of intra-arterial pressure signals can be derived from various sources. Some examples can be given though these examples are not intended to limit the scope of the invention: Firstly, said extra-arterial pressure signals are arterial applanation pressure signals, being transformable into pressure-related signals indicative of intra-arterial pressure signals. Secondly, said extra-arterial pressure signals are pulse oxymetry signals, being transformable into pressure-related signals indicative of intra-arterial pressure signals. Thirdly, said extra-arterial pressure signals are any physiological signals, being transformable into pressure-related signals indicative of intra-arterial pressure signals.

[0654] The significance of the procedure described in FIG. 9 has been shown for different types of test recordings. By using the method described in FIG. 9 it became possible to obtain nearly identical time sequence (TS.x) parameters (e.g. TS.MeandP, TS.MeanWavedP), independent whether the pressure sensor was placed within the epidural space or intra-dural. Thus, this feature of the invention makes it possible to measure intracranial pressure (ICP) by placing a sensor within the epidural space instead of within the brain tissue itself (intra-dural). This represents a major advantage since epidural placement of a sensor is less invasive with minimal opportunity of damaging the brain. This aspect of the invention is further commented on with reference to FIGS. 7a and 7b.

[0655] In test recordings using the method described in FIG. 9, it also was possible to compute nearly identical time sequence (TS.x) parameters whether location of intracranial pressure (ICP) signals was intra-dural within the brain parenchyma or within the spinal (lumbar) cerebrospinal fluid (CSF) cavity. Thus, it was possible to precisely measure intracranial pressure (ICP) by placing a cannula within the lumbar cerebrospinal fluid (CSF) cavity. This represents a great advantage, since the procedure is less invasive.

[0656] In test recordings it was also possible to obtain nearly identical time sequence (TS.x) parameters by a sensor placed within the outer ear channel as by placing the sensor intra-durally within the brain parenchyma. Aspects of this procedure are further described in FIGS. 10 and 11. Obviously it will be a great advantage to be able to measure intracranial pressure (ICP) by placing a sensor within the outer ear channel rather than by placing a sensor within the brain parenchyma or within the epidural space.

[0657] These examples are not intended to limit the scope of the invention. A major application is within the field of arterial blood pressure (ABP) monitoring, without placing a sensor within the blood vessel itself. Nevertheless, test recordings show that the method described in connection with FIG. 9 provides for a strategy of measuring pressures inside a body or body cavity by placing the sensor outside said body or body cavity.

[0658] Reference is now given to FIGS. 7a and 7b, to further illustrate the concept of computing related time sequence (rTS.x) parameters on the basis of time sequence (TS.x)-related parameters of simultaneous signals (corresponding to related time sequence parameters 926 and time sequence parameters 924, 925 of Signal[1] 902 and Signal[2] 903; FIG. 9). In FIG. 7a is shown a continuous intracranial intra-dural pressure (ICP) signal 701 derived from a sensor within the brain parenchyma (Signal[1] 701) (Recording[62].Signal[1].ICP.Intra-dural), and in FIG. 7b is shown a continuous intracranial epidural pressure (ICP) signal 702 derived from a sensor within the epidural space (Signal[2] 702) (Recording[62].Signal[2].ICP.Epidural). Thus, both signals Signal[1] 902 and Signal[2] 903 were from the same recording (Recording[62]), and were sampled simultaneously with identical time reference, thus with identical time scale 703 for both Signal[1] 701 and Signal[2] 702. The pressure scale 704 of Signal[1] 701 (FIG. 7a) and the pressure scale 705 of Signal[2] 702 (FIG. 7b) had identical resolution though the absolute pressure levels were different. For both signals are shown two subsequent time sequence windows, termed Time Sequence[30] 706 (n−1) and Time Sequence[31] 707 (n). These were included time sequence windows (see FIG. 9). For Signal[1] 701 the amplitudes (SW.dP) of the included single pressure waves 708 within the first time sequence window (Time Sequence[30] 706) are numbered 709 (SW[1].dP), 710 (SW[2].dP), 711 (SW[3].dP), 712 (SW[4].dP), 713 (SW[5].dP), and 714 (SW[6].dP). For Signal[1] 701 the amplitudes (SW.dP) of the seven included single pressure waves 708 within the second time sequence window (Time Sequence[31] 707) are numbered 715 (SW[1].dP), 716 (SW[2].dP), 717 (SW[3].dP), 718 (SW[4].dP), 719 (SW[5].dP), 720 (SW[6].dP) and 721 (SW[7].dP). For Signal[2] 702 the amplitudes (SW.dP) of the six included single pressure waves 708 within the first time sequence window (Time Sequence[30] 706) are numbered 722 (SW[1].dP), 723 (SW[2].dP), 724 (SW[3].dP), 725 (SW[4].dP), 726 (SW[5].dP), and 727 (SW[6].dP). For Signal[2] 702 the amplitudes (SW.dP) of the seven included single pressure waves 708 within the second time sequence window (Time Sequence[31] 707) are numbered 728 (SW[1].dP), 729 (SW[2].dP), 730 (SW[3].dP), 731 (SW[4].dP), 732 (SW[5].dP), 733 (SW[6].dP) and 734 (SW[7].dP).

[0659] FIGS. 7a and 7b show two simultaneous continuous pressure-related signals 701, 702 derived from different locations that are converted into pressure-related digital data with identical time reference 703. For each of said simultaneous time sequence windows (either Time Sequence[30] 706 or Time Sequence[31] 707) are identified the single pressure waves related to cardiac beat-induced pressure waves within said two or more simultaneous signals (Signal[1] 701 and Signal[2] 702) of a pressure recording. Some of said time sequence (TS.x)-related parameters of said single pressure waves 708 during said identical time sequence windows (either Time Sequence[30] 706 or Time Sequence[31] 707) within said two simultaneous signals (Signal[1] 701 and Signal[2] 702) of a pressure recording, are listed in Table 7.

[0660]

TABLE 7
Data related to time sequence windows shown in FIGS. 7a and 7b.
	Time sequence (TS.x)-related parameters
	TS.MeanWavedP	TS.MeanWavedT	TS.MeandP	TS.MeandT
Time Sequence[30]
Signal[1]	5.0 mmHg	0.265 sec	5.5 mmHg	0.265 sec
Signal[2]	5.9 mmHg	0.275 sec	5.7 mmHg	0.275 sec
Time Sequence[31]
Signal[1]	5.1 mmHg	0.268 sec	5.2 mmHg	0.268 sec
Signal[2]	5.9 mmHg	0.276 sec	6.0 mmHg	0.276 sec
	Related time sequence (rTS.x) parameter values
	rTS.MeanWavedP	rTS.MeanWavedT	rTS.MeandP	rTS.MeandT
Time Sequence[30]	0.85	0.96	0.96	0.96
	(=5.0/5.9)	(=0.265/0.275)	(=5.5/5.7)	(=0.265/0.275)
Time Sequence[31]	0.86	0.97	0.87	0.97
	(=5.1/5.9)	(=0.268/0.276)	(=5.2/6.0)	(=0.268/0.276)

[0661] With reference to Table 7, some comments should be made: Firstly, it should be noted that the time sequence (TS.x)-related parameters TS.MeanWavedP and TS.MeandP were almost identical, and also the time sequence (TS.x)-related parameters TS.MeanWavedT and TS.MeandT were identical. Secondly, the various time sequence (TS.x)-related parameters changed marginally between Time Sequence[30] and Time Sequence[31]. Thirdly, the related time sequence (rTS.x) parameters were constant relationships between said time sequence (TS.x)-related parameters. These two simultaneous time sequence windows (Time Sequence[30] 706 or Time Sequence[31] 707) were selected from a group of 3100 included time sequence windows (Time Sequence[1] to Time Sequence[3100]) of a recording with originally 3600 time sequence windows within each of said two signals (Signal[1] and Signal[2]) constituting the recording (Recording[62]).

[0662] To explain in more detail the results shown in Table 7 and FIG. 7, this specific example is now explained with reference to FIG. 9 wherein reference numbers to FIG. 9 is given in parenthesis. This is done in order to further clarify the flow chart of FIG. 9. The recording shown in FIGS. 7a and 7b Recording[62] (901) included two signals: Recording[62].Signal[1].ICP.Intra-dural (902); Recording[62].Signal[2].ICP.Epidural (903). Within each of said signals (902, 903) were a total of 3600 time sequence windows. After the Identifying Steps indicated in FIG. 9, a total of 3100 included time sequence windows (920, 921) were determined within each of said signals (902, 903) [i.e. within each signal (902, 903) a total of 500 excluded time sequence windows 922, 923 were determined]. For each of said 3100 included, i.e. accepted, time sequence windows Time Sequence[1] to Time Sequence[3100] (920, 921), the time sequence (TS.x)-related parameters (924, 925) TS.MeanWavedT and TS.MeandT were determined, and the related time sequence (rTS.x) parameters (926) rTS.MeanWavedT and rTS.MeandT were computed for each individual of said 3100 time sequence windows (920, 921). Subsequently, the mean value of these parameters (926) were determined for all included time sequence windows (920, 921) of said pressure recording Recording[62] (901). For both the related time sequence (rTS.x) parameters (926) rTS.MeanWavedP and rTS.MeandP, the mean value was 0.86. Furthermore, eight test recordings were made (Recording[61], Recording[62], . . . Recording[68]) with similar simultaneous intra-dural and epidural signals as in Recording[62]: Recording[62].Signal[1].ICP.Intra-dural; Recording[62].Signal[2].ICP.Epidural. Said population of recordings was categorized according to signal type since only simultaneous intra-dural and epidural signals were included in said recordings (901). For this population of recordings, the related time sequence (rTS.x) parameters (926) rTS.MeanWavedP and rTS.MeandP were determined according to equation (84).

[0663] Further aspects of determination of these related time sequence (rTS.x) parameters will now be explained. Some general comments are made concerning related time sequences (rTS.x) and factorized time sequences (fTS.x) with focus on notation. Through the invention it has been found useful to include separate notation related to said related time sequences (rTS.x) and factorized time sequences (fTS.x). Simultaneously included time sequences from two or more simultaneous signals are termed Perfect Time Sequences (i.e.PerfectTS). Perfect time sequences (PerfectTS) only include identical included time sequences from two or more signals wherein time reference is identical between all included time sequences. So-called Perfect Recording includes said Perfect Time Sequences, giving this notation: PerfectRecording[s].Signal [m].PerfectTS[u].

[0664] The term PopulationCount (PopCount) relates to a defined group of Perfect Time Sequences (PerfectTS[u]), wherein said defined group is determined by attributes such as signal type, location and sensor type.

[0665] In general, a related time sequence (rTS) is defined by equation (82):
rTS=ƒ(x)   (82)

[0666] According to this equation, rTS is a function (ƒ) of input x, wherein x is Perfect time sequences (PerfectTS) in general.

[0667] One example of input (x) is shown and expressed in equation (83):
ƒ(x)=ƒ1(PerfectTS.MeanWavedP)+ƒ2(PerfectTS.MeanWavedP_—STD)+ƒn(PerfectTS.x)+ . . .  (83)

[0668] The example shown in equation (83) indicates that input (x) can be a function of several Perfect time sequence (PerfectTS)-parameters.

[0669] In another example only the parameter PerfectTS.MeanWavedP is used as input, among all possible PerfectTS.x parameters, as detailed in equation (84). rTS.MeandP=

[0670] $\begin{array}{cc}\left(\sum _{u=1}^{\mathrm{PopCount}}\frac{\mathrm{ICP}·\mathrm{Epidural}·\mathrm{PerfectTS}\left[u\right]\mathrm{Mean}dP}{\mathrm{ICP}·\mathrm{Intra}-\mathrm{dural}·\mathrm{PerfectTS}\left[u\right]\mathrm{Mean}dP}\right)/\mathrm{PopCount}& \left(84\right)\end{array}$

[0671] The specific example given with reference to FIGS. 7a and 7b used this specific formula for determining the related time sequence parameter rTS.MeandP.

[0672] It should be noted as well that rTS[=ƒ(x)] can also be described with a much more advanced mathematical method and formula-based relationships between the individual Perfect time sequence (PerfectTS.x) parameters. The specific examples shown here are intended to illustrate the concept, not to limit the scope of the invention.

[0673] Some general comments are also given with reference to the factorized time sequences. In general a factorized time sequence (fTS) can be expressed according to equation (85):
fTS[o]=ƒ(x)  (85)

[0674] A factorized time sequence (fTS[o]) is a function of input x, wherein x is an input signal, as specified in equation (86):
fTS[o]=ƒ(Recording.Signal[m])  (86)

[0675] In this example, the input signal x is Recording.Signal[m], which is a specified signal (Signal[m]) in a non-specified recording.

[0676] In another example, the input signal x is Signal.TS[o], which is a specific perfect time sequence (PerfectTS[u]) in a non-specific signal, as shown in equation (87):
fTS[o]=ƒ(Signal.PerfectTS[u])  (87)

[0677] These equations illustrate the concept that a factorized time sequence (fTS) is a result of a function that contains a method and/or formula based on related time sequence (rTS) observations. Said method (formula) can be based on one or more of said PerfectTS.x-related parameters. Said factorized time sequences (fTS) thus can be considered as predicted time sequences as well.

[0678] Reference is again given to the population of test recordings (Recording[61] to Recording[68]) with similar simultaneous intra-dural and epidural signals as in Recording[62]: Recording[62].Signal[1].ICP.Intra-dural; Recording[62].Signal[2].ICP.Epidural. As already commented on, the related time sequence (rTS.x) parameters (926) rTS.MeanWavedP and rTS.MeandP were determined according to equation (84) for this population of recordings. The mean value for said population of recordings of related time sequence (rTS.x) parameters (926) rTS.MeanWavedP and rTS.MeandP were 0.83 and 0.87, respectively. Said population of recordings could also enable determination of population-based formulas for said related time sequence (rTS.x)-related parameters (926). It should be noted that the related time sequence (rTS.x) parameters rTS.MeanWavedP and rTS.MeandP only establish relationships for the pressure scale of said pressure recordings. In this context is as well explained the process of determining factorized time sequence (fTS.x) parameters, derived from related time sequence (rTS.x) values (926) of continuous pressure-related signals of individual pressure recordings. According to this example, factorized time sequence (fTS.x) parameters could be derived from related time sequence (rTS.x) values (926) of continuous pressure-related signals of a population of pressure recordings (Recording[61] to Recording[68]). For this population, rTS.MeanWavedP was equal to 0.83 and rTS.MeandP equal to 0.87. Since the epidural continuous signals are so-called non-gold standard signals, it may be useful factorize the time sequence parameters of these epidural signals. Factorization of such a specific signal is performed as follows: For said signal (903) all the included time sequence windows (921) are determined according to the Identifying Steps shown in FIG. 9, and time sequence parameters (925) determined for said included time sequence windows (921). For example, a factorized time sequence (fTS.x) parameter fTS.MeandP is created by multiplying the time sequence TS.[x].MeandP parameter of each of said included time sequence windows with the value 0.87 (see equation 87). This is only one example of creating factorized time sequence (fTS.x)-related parameters, and this specific example is not intended to limit the scope of the invention. The fTS.MeandP could as another example be calculated upon a multiple parameter relation such as rTS.x[1], rTS.x[2] . . . , wherein x[1] is the first related parameter and x[2] is the second related parameter. It is referred to equations 85 and 86.

[0679] Said creation of factorized time sequence (fTS.x) parameters relate to formula-based adjustment of time sequence (TS.x)-related parameters of individual time sequence windows of continuous pressure-related signals, said factorized time sequence (fTS.x) parameters can be selected from the group of:

• • [0680] factorized mean value of starting diastolic minimum pressure of a time sequence window (fTS.MeanP_min1),
  • [0681] factorized standard deviation of mean value of starting diastolic minimum pressure of a time sequence window (fTS.MeanP_min1—STD),
  • [0682] factorized mean value of systolic maximum pressure of a time sequence window (fTS.MeanP_max),
  • [0683] factorized standard deviation of mean value of systolic maximum pressure of a time sequence window (fTS.MeanP_max—STD),
  • [0684] factorized mean amplitude of a time sequence window (fTS.MeandP),
  • [0685] factorized standard deviation of mean amplitude of a time sequence window (fTS.MeandP_STD),
  • [0686] factorized mean latency of a time sequence window (fTS.MeandT),
  • [0687] factorized standard deviation of mean latency of a time sequence window (fTS.MeandT_STD),
  • [0688] factorized mean rise time coefficient of a time sequence window (fTS.MeanRT),
  • [0689] factorized standard deviation of mean rise time coefficient of a time sequence window (fTS.MeanRT_STD),
  • [0690] factorized mean wave duration of a time sequence window (fTS.MeanWD),
  • [0691] factorized standard deviation of mean wave duration of a time sequence window (fTS.MeanWD_STD),
  • [0692] factorized mean single wave pressure of a time sequence window (fTS.Mean_SWP),
  • [0693] factorized standard deviation of mean single wave pressure of a time sequence window (fTS.Mean_SWP_STD),
  • [0694] factorized mean value of diastolic minimum pressure difference of a time sequence window (fTS.MeanDiff_P_min),
  • [0695] factorized standard deviation of mean value of diastolic minimum pressure difference of a time sequence window (fTS.MeanDiff_P_min—STD),
  • [0696] factorized mean value of systolic maximum pressure difference of a time sequence window (fTS.MeanDiff_P_max),
  • [0697] factorized standard deviation of mean value of systolic maximum pressure difference of a time sequence window (fTS.MeanDiff_P_max—STD),
  • [0698] factorized mean amplitude difference of a time sequence window (fTS.MeanDiff_dP),
  • [0699] factorized standard deviation of mean amplitude difference of a time sequence window (fTS.MeanDiff_dP_STD),
  • [0700] factorized mean latency difference of a time sequence window (fTS.MeanDiff_dT),
  • [0701] factorized standard deviation of mean latency difference of a time sequence window (fTS.MeanDiff_dT_STD),
  • [0702] factorized mean rise time coefficient difference of a time sequence window (fTS.MeanDiff_RT),
  • [0703] factorized standard deviation of mean rise time coefficient difference of a time sequence window (fTS.MeanDiff_RT_STD),
  • [0704] factorized mean wave duration difference of a time sequence window (fTS.MeanDiff_WD),
  • [0705] factorized standard deviation of mean wave duration difference of a time sequence window (fTS.MeanDiff_WD_STD),
  • [0706] factorized standard deviation of mean single wave pressure difference of a time sequence window (fTS.MeanDiff_Mean_SWP_STD),
  • [0707] factorized amplitude of the mean wave of a time sequence window (fTS.MeanWavedP),
  • [0708] factorized latency of the mean wave of a time sequence window (fTS.MeanWavedT), and
  • [0709] factorized rise time coefficient of the mean wave of a time sequence window (fTS.MeanWaveRT).

[0710] Said formula-based adjustment of time sequence (TS.x)-related parameters can be related to multiplication of the pressure scale of said individual time sequence windows of said pressure-related signal with a given constant factor value derived from the related time sequence (rTS.x) parameters. Said formula-based adjustment of time sequence (TS.x)-related parameters can also be related to adjustment of the pressure scale of said individual time sequence windows of said pressure-related signal according to a formula relationship derived from the related time sequence (rTS.x) parameters.

[0711] Reference is now given to FIGS. 10a, 10b, 10c, and 10d in order to further give details about analyzing simultaneous continuous pressure-related signals derived from different locations inside and/or outside of a body and/or body cavity, with determination of related time sequence (rTS.x) parameters, and using said related time sequence (rTS.x) parameters for creating factorized time sequence (fTS.x) parameters. FIG. 10 relates to simultaneous sampling of pressure-related signals derived from within the brain parenchyma (Signal[1]) and from the outer ear channel (Signal[2]) after air tight sealing of said outer ear channel.

[0712] In paragraphs [278] to [280] is given reference to a fourth feature of this invention. Said fourth feature of the invention relates to a device for use in sensing continuous pressure-related signals through non-invasive pressure measurements on a human body, comprising a pressure sensor with a pressure sensing tube, said tube insertable into a human or animal outer ear channel spaced from a tympanic membrane thereof, and inflatable means surrounding an outside length of the tube, said inflatable means upon inflation thereof sealingly closing an annular gap between a region of said tube and a wall region of said outer ear channel. Reference is now given to FIG. 10a, showing in detail such a device for obtaining air pressure signals from within an airtight sealed outer ear channel. In FIG. 10a is shown a schematic presentation of an ear 1001, wherein ear skin surface forms the outer wall of the outer ear channel 1002 and the middle ear 1003, separated by the tympanic membrane 1004. Continuous pressure-related signals indicative of intracranial pressure are obtained by a sensor 1005 measuring air pressure within the outer ear channel 1002. A pressure sensing tube 1006 enables the sensor 1005 to be in contact with the closed air chamber within the outer ear channel 1002. This outer ear channel 1002 is closed by an inflatable means 1007 that is an inflatable thin walled balloon within the ear 1001 and the outer ear channel 1002, surrounding the pressure sensing tube 1006. Said inflatable balloon 1007 gives air tight sealing of the outer ear channel 1002 by being filled with fluid using a release duct 1008. Fluid could be e.g. air or water. Fluid filling of said inflatable balloon 1007 enables closing of the outer ear channel 1002. Thereby the air within the outer ear channel 1002 is separated from the atmospheric pressure. Pressure fluctuations are created by movement of the tympanic membrane 1004, since the tympanic membrane 1004 moves along with the pressure fluctuations within the middle ear 1003. The pressure gradient 1009 for each pressure wave is indicated with an arrow. Said inflatable means 1007 is deflatable by opening the release duct 1008, or by puncturing the inflatable means 1007. The non-invasive ear channel related sensor 1005 is connected via a signal transducer 1010 to a processing unit 1011, said processing unit 1011 being capable of delivering continuous pressure-related signals. It must be understood that said pressure sensor 1005 could be located anywhere along said pressure sensing tube 1006, said sensor 1005 location being within the ear channel 1002 or outside the ear channel 1002.

[0713] The significance of this device is that said inflatable means 1007 is thin-walled and soft, thus making airtight sealing of an outer ear channel 1002 possible, independent on the diameter of the outer ear channel 1002. Thereby one size of said sensor may be used independent of the diameter of the outer ear channel.

[0714] The opportunity to measure air pressure within the outer ear channel 1002 after airtight sealing of said channel 1002 is known in the prior art, based on the knowledge that is described in this paragraph. The intracranial pressure waves are transmitted via fluid pathways from the subarachnoid space to the perilymphatic duct (cochlear aqueduct). This pathway is the primary conduit between the subarachnoid space and the perilymphatic spaces, and is connected to the inner ear where the pressure waves cause motion of the oval (and round) window and the ossicles, leading to motion of the tympanic membrane 1004. If the outer ear channel 1002 is sealed in an airtight fashion, these motions of the tympanic membrane 1004 cause air-pressure fluctuations 1009 that can be recorded using a special sensor 1005.

[0715] Reference is now given to FIGS. 10b and 10c, in order to illustrate processing of two simultaneous continuous pressure-related signals, in this case derived from within the brain parenchyma (Signal[1] 1012) and from the outer ear channel 1002 (Signal[2] 1013), as described in detail with reference to FIG. 10a. This particular example is taken from a test recording Recording[241]. It is shown single pressure waves 1014 of Signal[1] and single pressure waves of Signal[2] 1015. Both signals 1012, 1013 have identical time reference, indicated by identical time scale 1016 for both signals 1012, 1013. It is evident that the pressure scales of Signal[1] 1017 and Signal[2] 1018 are different with different scaling, related to the fact that said signals 1012, 1013 are measured with different sensors, having different characteristics. The time sequence window 1019 of Signal[1] and the time sequence window 1020 of Signal[2] are identical according to time and is referred to as Recording[241].Signal[1].Time Sequence[12] and Recording[241].Signal[2].Time Sequence[12]. Within said time sequence windows 1019, 1020 (Time Sequence[12] or TS[12]), single pressure waves 1014, 1015 related to cardiac beat-induced pressure waves within said two simultaneous signals 1012, 1013 are identified. With reference to FIG. 10b, it is indicated that included pair combinations of valleys and peaks in said signal 1012 are identified, corresponding to included pair combinations of a systolic maximum pressure value (SW.P_max) 1021 a diastolic minimum pressure value (SW.P_min1) 1022, characterizing single pressure waves 1014 created by the cardiac beat-induced pressure waves. Within this signal 1012 the ending diastolic minimum pressure (SW.P_min2) 1023 of a first single pressure wave was the same as starting diastolic minimum pressure (SW.P_min1) 1022 of the subsequent second single pressure wave. The systolic maximum pressures (SW.P_max) 1021 are indicated as filled squares, and the diastolic minimum pressures (P_min1/P_min2) 1022, 1023 are shown as open circles. With reference to FIG. 10c, it is as well indicated for Signal[2] 1013 the included pair combinations of a systolic maximum pressure value (SW.P_max) 1024 a diastolic minimum pressure value (SW.P_min1) 1025. Also for this fragment of the signal 1013, the ending diastolic minimum pressure (SW.P_min2) 1026 of a first single pressure wave was the same as starting diastolic minimum pressure (SW.P_min1) 1025 of the subsequent second single pressure wave. For each of said time sequence windows 1019, 1020 (Time Sequence[12]), were computed the time sequence (TS.x)-related parameters TS.MeandP and TS.MeanWavedP, as well as the related time sequence (rTS.x) parameters rTS.MeandP and rTS.MeanWavedP, as shown in Table 8.

[0716]

TABLE 8
Data related to time sequence windows shown in FIGS. 10b and 10c.
	Time sequence
	(TS.x)-related parameters
		TS.MeanWavedP	TS.MeandP
	Signal[1]	2.75 mmHg	2.82 mmHg
	Signal[2]	20.7 mmHg	21.46 mmHg
	Related time
	sequence (rTS.x) values
	rTS.MeanWavedP	rTS.MeandP
	0.133	0.131
	(=2.75/20.7)	(=2.82/21.46)

[0717] The recording incorporating Signal[1] 1012 and Signal[2] 1013 included a total of 500 time sequence windows (Time Sequence[1] to Time Sequence[500]) within each of said signals 1012, 1013 (Signal[1] and Signal[2]). The method for processing said signal 1012, 1013 illustrated by the Identifying Steps in FIG. 9 were applied said 500 time sequence windows, and only 40 time sequence windows [referred to as PerfectTimeSequences (PerfectTS)] were included since strict Single Wave & Delta Single Wave Criteria and Time Sequence & Delta Time Sequence Criteria were used. For these 40 included time sequence windows (PerfectTS) both the related time sequence (rTS.x) parameters rTS.MeanWavedP and rTS.MeandP were computed. The mean value of rTS.MeandP of said 40 time sequence windows was 0.126. This value was subsequently used for creating fTS.MeandP. This aspect of the method is further illustrated in FIG. 10d. The consecutive numbers of said 40 included time sequence windows (PerfectTS) are indicated on the x scale 1027. Pressure values are indicated on the y scale or pressure scale 1028. The trend plot of TS.MeandP 1029 of Signal[1] 1012 is indicated for said 40 included time sequence windows. In addition, the trend plot of fTS.MeandP 1030 of Signal[2] 1013 is indicated. Factorization of Signal[2] was performed by multiplying the parameter TS.MeandP of each individual of said 40 time sequence windows with the factor 0.126. When considering the trend plots in FIG. 10d, the mean value of all 40 time sequence windows of TS.MeandP 1029 of Signal[1] 1012 was 2.98±0.28 and the comparable mean value of mean fTS.MeandP 1030 of Signal[2] 1013 was 2.99±0.21. Thus, after factorization TS.MeandP of Signal[2] 1013 was nearly identical to TS.MeandP of Signal[1] 1012. Also the comparable fluctuations of TSMeandP 1029 and fTS.MeandP 1030 are shown in FIG. 10d.

[0718] The data presented in FIGS. 10b, 10c and 10d provide illustrations that computation of related (rTS.x) and factorized (fTS.x) time sequence parameters can be very useful for non-invasive pressure measurements. This inventive feature provides a technical solution to this yet unsolved problem related to non-invasive pressure measurements.

[0719] Reference is now given to FIGS. 11a and 11b that illustrate the same aspects as already described for FIGS. 10a, 10b and 10c. One individual recording contained two signals: Recording[250].Signal[1].ICP.Intradural; Recording[250].Signal[2].ICP.EarProbe. This means that both signals were continuous intracranial pressure (ICP)-related signals, wherein the sensor location was intra-dural (or within the brain parenchyma for Signal[1]) and non-invasive within the outer ear channel for Signal[2] within the same recording Recording[250]. The method of deriving continuous pressure-related signals indicative of intracranial pressure by measuring air pressure within the outer ear channel was described in detail for FIG. 10a. For the case referred to in FIGS. 11a and 11b, both Signal[1] and Signal[2] contained 3000 time sequence windows each (Time Sequence[1] to Time Sequence[3000]). The method described in detail for FIG. 9 was applied to both signals, leaving a total of 750 included and simultaneous time sequence windows within each of said two signals, i.e. 750 perfect time sequence windows (PerfectTS) were included. For both of said signals (Signal[1] and Signal[2]) the time sequence (TS.x) related parameter TS.MeandP were computed. Trend plots of TS.MeandP for these signals are shown in FIG. 11a, with numbers of consecutive included time sequence windows on the x scale 1101. The pressure scale 1102 is indicated on the y scale. Trend plot TS.MeandP 1103 of Signal[1] is indicated as well as trend plot TS.MeandP 1104 of Signal[2]. It is obvious that the y scaling of said two signals (Signal[1] and Signal[2]) are very different related to the fact that these signals were derived from different types of sensors and that the calibration of Signal[2] against atmospheric pressure was unknown. For each of these 750 simultaneous consecutive time sequence windows, the related time sequence (rTS.x) parameter rTS.MeandP was computed for each individual of said time sequence windows. The mean value of said 750 rTS.MeandP values was 0.137. Factorization of Signal [2] was performed by multiplying the parameter TS.MeandP of each individual of said 750 time sequences (PerfectTS) with the value 0.137, creating fTS.MeandP. The x scale 1101 is identical in FIGS. 11a and 11b. Concerning the y scale 1102, the scaling has been increased from in FIG. 11a to FIG. 11b. The trend plot of TS.MeandP 1103 of Signal[1] is indicated in FIG. 11b which is the same as signal 1103 in FIG. 11a. The trend plot of the factorized time sequence (fTS.x) parameter fTS.MeandP 1105 of Signal[2] (1104 in FIG. 11a) is as well shown in FIG. 11b. It should be noted that after factorization, the trend plots of TS.MeandP of 1103 Signal[1] and fTS.MeandP 1105 of Signal[2] followed each other closely, as clearly noted from FIG. 11b. When considering the trend plots in FIG. 11b, the mean value of all 750 time sequence windows (PerfectTS) of TS.MeandP 1103 of Signal[1] was 3.2±0.4 and the comparable mean value of mean fTS.MeandP 1105 of Signal[2] was 3.2±0.5. Thus, after factorization TS.MeandP of Signal[2] was practically identical to TS.MeandP of Signal[1]. Also the comparable fluctuations of TSMeandP 1103 and fTS.MeandP 1105 are shown in FIG. 11b.