US 7,366,572 B2Grant
Controlling therapy based on sleep quality
Issue Date:2008-04-29
•61 Claims
•8 Drawing Sheets
Abstract
A medical device, such as an implantable medical device (IMD), determines values for one or more metrics that indicate the quality of a patient's sleep, and controls delivery of a therapy based on the sleep quality metric values. For example, the medical device may compare a sleep quality metric value with one or more threshold values, and adjust the therapy based on the comparison. In some embodiments, the medical device adjusts the intensity of therapy based on the comparison, e.g., increases the therapy intensity when the comparison indicates that the patient's sleep quality is poor. In some embodiments, the medical device automatically selects one of a plurality of therapy parameter set available for use in delivering therapy based on a comparison sleep quality metric values associated with respective therapy parameter sets within the plurality of available therapy parameter sets.
Metadata
Assignee
- Medtronic, Inc.
Inventors
- Kenneth T. Heruth
- Keith A. Miesel
Application Information
Application Number:US 10/825,953
Filing Date:2004-04-15
Priority Date:2004-03-16
Art Unit:3766
Classifications
IPC:
A61N1/18
Patent Drawings (8 sheets)
Description
[0001] This application claims the benefit of U.S. Provisional Application No. 60/553,777, filed Mar. 16, 2004, which is incorporated herein by reference in its entirety.
Technical Field
[0002] The invention relates to medical devices and, more particularly, to medical devices that deliver a therapy.
Background
[0003] In some cases, an ailment that a patient has may affect the quality of the patient's sleep. For example, chronic pain may cause a patient to have difficulty falling asleep, and may disturb the patient's sleep, e.g., cause the patient to wake. Further, chronic pain may cause the patient to have difficulty achieving deeper sleep states, such as the nonrapid eye movement (NREM) sleep state. Other ailments that may negatively affect patient sleep quality include movement disorders, psychological disorders, sleep apnea, congestive heart failure, gastrointestinal disorders and incontinence. In some cases, these ailments are treated via an implantable medical device (IMD), such as an implantable stimulator or drug delivery device.
[0004] Further, in some cases, poor sleep quality may increase the symptoms experienced by a patient due to an ailment. For example, poor sleep quality has been linked to increased pain symptoms in chronic pain patients. The link between poor sleep quality and increased symptoms is not limited to ailments that negatively impact sleep quality, such as those listed above. Nonetheless, the condition of a patient with such an ailment may progressively worsen when symptoms disturb sleep quality, which in turn increases the frequency and/or intensity of symptoms.
Summary
[0005] In general, the invention is directed to techniques for controlling delivery of a therapy to a patient by a medical device, such as an implantable medical device (IMD), based on the quality of sleep experienced by a patient. In particular, a medical device according to the invention determines values for one or more metrics that indicate the quality of a patient's sleep, and controls delivery of a therapy based on the sleep quality metric values. A medical device according to the invention may be able to adjust the therapy to address symptoms causing disturbed sleep or symptoms that are worsened by disturbed sleep, such as chronic pain.
[0006] The medical device monitors one or more physiological parameters of the patient in order to determine values for the one or more sleep quality metrics. Example physiological parameters that the medical device may monitor include activity level, posture, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, core temperature, arterial blood flow, melatonin level within one or more bodily fluids, and galvanic skin response. In some embodiments, the medical device additionally or alternatively monitors the variability of one or more of these parameters. In order to monitor one or more of these parameters, the medical device may include, be coupled to, or be in wireless communication with one or more sensors, each of which outputs a signal as a function of one or more of these physiological parameters.
[0007] Sleep efficiency and sleep latency are example sleep quality metrics for which a medical device according to the invention may determine values. Sleep efficiency may be measured as the percentage of time while the patient is attempting to sleep that the patient is actually asleep. Sleep latency may be measured as the amount of time between a first time when begins attempting to sleep and a second time when the patient falls asleep.
[0008] The time when the patient begins attempting to fall asleep may be determined in a variety of ways. For example, the medical device may receive an indication from the patient that the patient is trying to fall asleep, e.g., via a patient programming device in embodiments in which the medical device is an implantable medical device. In other embodiments, the medical device may monitor the activity level of the patient, determining whether the patient has remained inactive for a threshold period of time, and identify the time at which the patient became inactive as the time at which the patient begin attempting to fall asleep. In still other embodiments, the medical device may monitor patient posture, and identify the time when the patient is recumbent, e.g., lying down, as the time when the patient is attempting to fall asleep. In these embodiments in which the medical device determines when the patient is recumbent, the medical device may also monitor patient activity, and the medical may confirm that the patient is attempting to sleep based on the patient's activity level.
[0009] As another example, the medical device may determine the time at which the patient begins attempting to fall asleep based on the level of melatonin within one or more bodily fluids, such as the patient's blood, cerebrospinal fluid (CSF), or interstitial fluid. The medical device may also determine a melatonin level based on metabolites of melatonin located in the saliva or urine of the patient. Melatonin is a hormone secreted by the pineal gland into the bloodstream and the CSF as a function of exposure of the optic nerve to light, which synchronizes the patient's circadian rhythm. In particular, increased levels of melatonin during evening hours may cause physiological changes in the patient, which, in turn, may cause the patient to attempt to fall asleep. The medical device may, for example, detect an increase in the level of melatonin, and estimate the time that the patient will attempt to fall asleep based on the detection.
[0010] The medical device may determine the time at which the patient has fallen asleep based on the activity level of the patient and/or one or more of the other physiological parameters that may be monitored by the medical device as indicated above. For example, a discernable change, e.g., a decrease, in one or more physiological parameters, or the variability of one or more physiological parameters, may indicate that the patient has fallen asleep. In some embodiments, the medical device may determine a sleep probability metric value based on a value of a physiological parameter. In such embodiments, the medical device may compare the sleep probability metric value to a threshold to identify when the patient has fallen asleep. In some embodiments, the medical device may determine a plurality of sleep probability metric values based on a value of each of a plurality of physiological parameters, average or otherwise combine the plurality of sleep probability metric values to provide an overall sleep probability metric value, and compare the overall sleep probability metric value to a threshold to identify the time that the patient falls asleep.
[0011] Other sleep quality metrics that the medical device may determine include total time sleeping per day, the amount or percentage of time sleeping during nighttime or daytime hours per day, and the number of apnea and/or arousal events per night. In some embodiments, the medical device may be able to determine which sleep state the patient is in, e.g., rapid eye movement (REM), or one of the nonrapid eye movement (NREM) states (S1, S2, S3, S4), and the amount of time per day spent in these various sleep states may be determined as a sleep quality metric. Because they provide the most “refreshing” type of sleep, the amount of time spent in one or both of the S3 and S4 sleep states, in particular, may be determined as a sleep quality metric. In some embodiments, the medical device may determine average or median values of one or more sleep quality metrics over greater periods of time, e.g., a week or a month, as the value of the sleep quality metric. Further, in embodiments in which the medical device collects values for a plurality of the sleep quality metrics identified above, the medical device may determine a value for an overall sleep quality metric based on the collected values for the plurality of sleep quality metrics.
[0012] The medical device controls delivery of therapy based on determined sleep quality metric values. For example, the medical device may compare a current, a mean, a median, or an overall sleep quality metric value with one or more threshold values, and adjust the therapy based on the comparison. In some embodiments, the medical device adjusts the intensity of therapy based on the comparison, e.g., increases the therapy intensity when the comparison indicates that the patient's sleep quality is poor. In embodiments in which the medical device is a neurostimulator, for example, the pulse amplitude, pulse width, pulse rate, or duty cycle of delivered neurostimulation can be adjusted. As another example, in embodiments in which the medical device is a pump, the dosage or infusion rate of a therapeutic agent delivered by the pump can be adjusted.
[0013] In some embodiments, the medical device delivers therapy according to a current set of therapy parameters. The current therapy parameter set may be a selected one of a plurality of therapy parameter sets specified by a clinician. The currently selected therapy one of these preprogrammed parameter sets may be selected by a processor of the medical device, e.g., according to a therapy schedule, or by the patient using a patient programmer. A current therapy parameter set may also be the result of the patient modifying one or more parameters of a preprogrammed parameter set. In either case, the medical device identifies the current therapy parameter set when a value of one or more sleep quality metrics is collected, and may associate that value with the current therapy parameter set.
[0014] For example, for each of a plurality of therapy parameter sets, the medical device may store a representative value of each of one or more sleep quality metrics in a memory with an indication of the therapy parameter set with which the representative value is associated. A representative value of sleep quality metric for a therapy parameter set may be the mean or median of collected sleep quality metric values that have been associated with that therapy parameter set. In some embodiments, the medical device controls delivery of therapy according to sleep quality metric values by automatically selecting one of the plurality therapy parameter sets for use in delivering therapy based on a comparison of their representative sleep quality metric values, e.g., automatically selects the therapy parameter set whose representative values indicate the highest sleep quality.
[0015] In one embodiment, the invention is directed to a method in which at least one physiological parameter of a patient is monitored via a medical device that delivers a therapy to a patient. A value of a metric that is indicative of sleep quality is determined based on the at least one physiological parameter. Delivery of the therapy by the medical device is controlled based on the sleep quality metric value.
[0016] In another embodiment, the invention is directed to a medical device comprising a therapy module to deliver a therapy to a patient and a processor. The processor monitors at least one physiological parameter of a patient based on at least one signal received from at least one sensor, determines a value of a metric that is indicative of sleep quality based on the at least one physiological parameter, and controls delivery of the therapy by the therapy module based on the sleep quality metric value.
[0017] In another embodiment, the invention is directed to a computer-readable medium containing instructions. The instructions cause a programmable processor to monitor at least one physiological parameter of a patient via a medical device delivers a therapy to the patient, determine a value of a metric that is indicative of sleep quality based on the at least one physiological parameter, and control delivery of the therapy by the medical device based on the sleep quality metric value.
[0018] The invention is capable of providing one or more advantages. For example, a medical device according to the invention may be able to adjust the therapy to address symptoms causing disturbed sleep, or symptoms that are worsened by disturbed sleep. Adjusting therapy based on sleep quality information may significantly improve the patient's sleep quality and condition. The ability of a medical device to adjust therapy based on sleep quality may be particularly advantageous in embodiments in which the medical device delivers the therapy to treat chronic pain, which may both disturb sleep and be worsened by disturbed sleep. Moreover, the ability of the medical device to both automatically identify a need for therapy adjustment and automatically adjust the therapy may reduce the need for the patient to make time consuming and expensive clinic visits when the patient's sleep is disturbed or symptoms have worsened.
[0019] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.
Brief Description of Drawings
[0020] FIG. 1 is a conceptual diagram illustrating an example system that includes an implantable medical device that controls delivery of therapy based on sleep quality information according to the invention.
[0021] FIG. 2 is a block diagram further illustrating the example system and implantable medical device of FIG. 1 .
[0022] FIG. 3 is a block diagram illustrating an example memory of the implantable medical device of FIG. 1 .
[0023] FIG. 4 is a flow diagram illustrating an example method for collecting sleep quality information that may be employed by an implantable medical device.
[0024] FIG. 5 is a flow diagram illustrating an example method for associating sleep quality information with therapy parameter sets that may be employed by a medical device.
[0025] FIG. 6 is a flow diagram illustrating an example method for controlling therapy based on sleep quality information that may be employed by a medical device.
[0026] FIG. 7 is a flow diagram illustrating another example method for controlling therapy based on sleep quality information that may be employed by a medical device.
Detailed Description
[0027] FIG. 1 is a conceptual diagram illustrating an example system 10 that includes an implantable medical device (IMD) 14 that controls delivery of a therapy to a patient 12 based on sleep quality information. In particular, as will be described in greater detail below, IMD 14 determines values for one or more metrics that indicate the quality of sleep experienced by patient 12, and controls delivery of the therapy based on the sleep quality metric values. IMD 14 may be able to adjust the therapy to address symptoms causing disturbed sleep, or symptoms that are worsened by disturbed sleep. In exemplary embodiments, IMD 14 delivers a therapy to treat chronic pain, which may both negatively impact the quality of sleep experienced by patient 12, and be worsened by inadequate sleep quality.
[0028] In the illustrated example system 10, IMD 14 takes the form of an implantable neurostimulator that delivers neurostimulation therapy in the form of electrical pulses to patient 12. IMD 14 delivers neurostimulation therapy to patient 12 via leads 16A and 16B (collectively “leads 16”). Leads 16 may, as shown in FIG. 1 , be implanted proximate to the spinal cord 18 of patient 12, and IMD 14 may deliver spinal cord stimulation (SCS) therapy to patient 12 in order to, for example, reduce pain experienced by patient 12.
[0029] However, the invention is not limited to the configuration of leads 16 shown in FIG. 1 , or to the delivery of SCS therapy. For example, one or more leads 16 may extend from IMD 14 to the brain (not shown) of patient 12, and IMD 14 may deliver deep brain stimulation (DBS) therapy to patient 12 to, for example, treat tremor or epilepsy. As further examples, one or more leads 16 may be implanted proximate to the pelvic nerves (not shown) or stomach (not shown), and IMD 14 may deliver neurostimulation therapy to treat incontinence or gastroparesis.
[0030] Moreover, the invention is not limited to implementation via an implantable neurostimulator, or even implementation via an IMD. For example, in some embodiments of the invention, an implantable or external or cardiac rhythm management device, such as a pacemaker, may control delivery of a therapy based on sleep quality information. In other words, any implantable or external medical device that delivers a therapy may control delivery of the therapy based on collected sleep quality information according to the invention.
[0031] In the example of FIG. 1 , IMD 14 delivers therapy according to a set of therapy parameters, i.e., a set of values for a number of parameters that define the therapy delivered according to that therapy parameter set. In embodiments where IMD 14 delivers neurostimulation therapy in the form of electrical pulses, the parameters for each parameter set may include voltage or current pulse amplitudes, pulse widths, pulse rates, and the like. Further, each of leads 16 includes electrodes (not shown in FIG. 1 ), and a therapy parameter set may include information identifying which electrodes have been selected for delivery of pulses, and the polarities of the selected electrodes. Therapy parameter sets used by IMD 14 may include a number of parameter sets programmed by a clinician (not shown), and parameter sets representing adjustments made by patient 12 to these preprogrammed sets.
[0032] In other non-neurostimulator embodiments of the invention, the IMD may still deliver therapy according to a therapy parameter set. For example, implantable pump IMD embodiments may deliver a therapeutic agent to a patient according to a therapy parameter set that includes, for example, a dosage, an infusion rate, and/or a duty cycle.
[0033] System 10 also includes a clinician programmer 20. A clinician (not shown) may use clinician programmer 20 to program therapy for patient 12, e.g., specify a number of therapy parameter sets and provide the parameter sets to IMD 14. The clinician may also use clinician programmer 20 to retrieve information collected by IMD 14. The clinician may use clinician programmer 20 to communicate with IMD 14 both during initial programming of IMD 14, and for collection of information and further programming during follow-up visits.
[0034] Clinician programmer 20 may, as shown in FIG. 1 , be a handheld computing device. Clinician programmer 20 includes a display 22, such as a LCD or LED display, to display information to a user. Clinician programmer 20 may also include a keypad 24, which may be used by a user to interact with clinician programmer 20. In some embodiments, display 22 may be a touch screen display, and a user may interact with clinician programmer 20 via display 22. A user may also interact with clinician programmer 20 using peripheral pointing devices, such as a stylus, mouse, or the like. Keypad 24 may take the form of an alphanumeric keypad or a reduced set of keys associated with particular functions.
[0035] System 10 also includes a patient programmer 26, which also may, as shown in FIG. 1 , be a handheld computing device. Patient 12 may use patient programmer 26 to control the delivery of therapy by IMD 14. For example, using patient programmer 26, patient 12 may select a current therapy parameter set from among the therapy parameter sets preprogrammed by the clinician, or may adjust one or more parameters of a preprogrammed therapy parameter set to arrive at the current therapy parameter set.
[0036] Patient programmer 26 may also include a display 28 and a keypad 30 to allow patient 12 to interact with patient programmer 26. In some embodiments, display 28 may be a touch screen display, and patient 12 may interact with patient programmer 26 via display 28. Patient 12 may also interact with patient programmer 26 using peripheral pointing devices, such as a stylus, mouse, or the like.
[0037] However, clinician and patient programmers 20, 26 are not limited to the hand-held computer embodiments illustrated in FIG. 1 . Programmers 20, 26 according to the invention may be any sort of computing device. For example, a programmer 20, 26 according to the invention may a tablet-based computing device, a desktop computing device, or a workstation.
[0038] IMD 14, clinician programmer 20 and patient programmer 26 may, as shown in FIG. 1 , communicate via wireless communication. Clinician programmer 20 and patient programmer 26 may, for example, communicate via wireless communication with IMD 14 using radio frequency (RF) telemetry techniques known in the art. Clinician programmer 20 and patient programmer 26 may communicate with each other using any of a variety of local wireless communication techniques, such as RF communication according to the 802.11 or Bluetooth specification sets, infrared communication according to the IRDA specification set, or other standard or proprietary telemetry protocols.
[0039] Clinician programmer 20 and patient programmer 26 need not communicate wirelessly, however. For example, programmers 20 and 26 may communicate via a wired connection, such as via a serial communication cable, or via exchange of removable media, such as magnetic or optical disks, or memory cards or sticks. Further, clinician programmer 20 may communicate with one or both of IMD 14 and patient programmer 26 via remote telemetry techniques known in the art, communicating via a local area network (LAN), wide area network (WAN), public switched telephone network (PSTN), or cellular telephone network, for example.
[0040] As mentioned above, IMD 14 controls delivery of a therapy, e.g., neurostimulation, to patient 12 based on the quality sleep experienced by the patient. In particular, as will be described in greater detail below, IMD 14 determines values for one or more metrics that indicate the quality of sleep experienced by patient 12. IMD 14 controls delivery of the therapy to patient 12, e.g., adjusts the therapy, based on the sleep quality metric values.
[0041] IMD 14 monitors one or more physiological parameters of the patient in order to determine values for the one or more sleep quality metrics. Example physiological parameters that IMD 14 may monitor include activity level, posture, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid (CSF), muscular activity, core temperature, arterial blood flow and the level of melatonin within one or more bodily fluids. Some external medical device embodiments of the invention may additionally or alternatively monitor galvanic skin response. Further, in some embodiments IMD 14 additionally or alternatively monitors the variability of one or more of these parameters. In order to monitor one or more of these parameters, IMD 14 may include, be coupled to, or be in wireless communication with one or more sensors (not shown in FIG. 1 ), each of which outputs a signal as a function of one or more of these physiological parameters.
[0042] For example, IMD 14 may determine sleep efficiency and/or sleep latency values. Sleep efficiency and sleep latency are example sleep quality metrics. IMD 14 may measure sleep efficiency as the percentage of time while patient 12 is attempting to sleep that patient 12 is actually asleep. IMD 14 may measure sleep latency as the amount of time between a first time when patient 12 begins attempting to sleep and a second time when patient 12 falls asleep, e.g., as an indication of how long it takes patient 12 to fall asleep.
[0043] IMD 14 may identify the time at which patient begins attempting to fall asleep in a variety of ways. For example, IMD 14 may receive an indication from the patient that the patient is trying to fall asleep via patient programmer 26. In other embodiments, IMD 14 may monitor the activity level of patient 12, and identify the time when patient 12 is attempting to fall asleep by determining whether patient 12 has remained inactive for a threshold period of time, and identifying the time at which patient 12 became inactive. In still other embodiments, IMD 14 may monitor the posture of patient 12, and may identify the time when the patient 12 becomes recumbent, e.g., lies down, as the time when patient 12 is attempting to fall asleep. In these embodiments, IMD 14 may also monitor the activity level of patient 12, and confirm that patient 12 is attempting to sleep based on the activity level.
[0044] As another example, IMD 14 may determine the time at which patient 12 is attempting to fall asleep based on the level of melatonin within one or more bodily fluids of patient 12, such as the patient's blood, cerebrospinal fluid (CSF), or interstitial fluid. IMD 14 may also determine a melatonin level based on metabolites of melatonin located in the saliva or urine of the patient. Melatonin is a hormone secreted by the pineal gland into the bloodstream and the CSF as a function of exposure of the optic nerve to light, which synchronizes the patient's circadian rhythm. In particular, increased levels of melatonin during evening hours may cause physiological changes in patient 12, which, in turn, may cause patient 12 to attempt to fall asleep.
[0045] IMD 14 may, for example, detect an increase in the level of melatonin in a bodily fluid, and estimate the time that patient 12 will attempt to fall asleep based on the detection. For example, IMD 14 may compare the melatonin level or rate of change in the melatonin level to a threshold level, and identify the time that threshold value is exceeded. IMD 14 may identify the time that patient 12 is attempting to fall asleep as the time that the threshold is exceeded, or some amount of time after the threshold is exceeded.
[0046] IMD 14 may identify the time at which patient 12 has fallen asleep based on the activity level of the patient and/or one or more of the other physiological parameters that may be monitored by IMD 14 as indicated above. For example, IMD 14 may identify a discernable change, e.g., a decrease, in one or more physiological parameters, or the variability of one or more physiological parameters, which may indicate that patient 12 has fallen asleep. In some embodiments, IMD 14 determines a sleep probability metric value based on a value of a physiological parameter monitored by the medical device. In such embodiments, the sleep probability metric value may be compared to a threshold to identify when the patient has fallen asleep. In some embodiments, a sleep probability metric value is determined based on a value of each of a plurality of physiological parameters, the sleep probability values are averaged or otherwise combined to provide an overall sleep probability metric value, and the overall sleep probability metric value is compared to a threshold to identify the time that the patient falls asleep.
[0047] Other sleep quality metrics include total time sleeping per day, and the amount or percentage of time sleeping during nighttime or daytime hours per day. In some embodiments, IMD 14 may be able to detect arousal events and apneas occurring during sleep based on one or more monitored physiological parameters, and the number of apnea and/or arousal events per night may be determined as a sleep quality metric. Further, in some embodiments IMD 14 may be able to determine which sleep state patient 12 is in based on one or more monitored physiological parameters, e.g., rapid eye movement (REM), S1, S2, S3, or S4, and the amount of time per day spent in these various sleep states may be a sleep quality metric.
[0048] The S3 and S4 sleep states may be of particular importance to the quality of sleep experienced by patient 12. Interruption from reaching these states, or inadequate time per night spent in these states, may cause patient 12 to not feel rested. For this reason, the S3 and S4 sleep states are believed to provide the “refreshing” part of sleep.
[0049] In some cases, interruption from reaching the S3 and S4 sleep states, or inadequate time per night spent in these states has been demonstrated to cause normal subjects to exhibit some symptoms of fibromyalgia. Also, subjects with fibromyalgia usually do not reach these sleep states. For these reasons, in some embodiments, IMD 14 may determine an amount or percentage of time spent in one or both of the S3 and S4 sleep states as a sleep quality metric.
[0050] In some embodiments, IMD 14 may determine average or median values of one or more sleep quality metrics over greater periods of time, e.g., a week or a month, as the value of the sleep quality metric. Further, in embodiments in which IMD 14 collects values for a plurality of the sleep quality metrics identified above, IMD 14 may determine a value for an overall sleep quality metric based on the collected values for the plurality of sleep quality metrics. IMD 14 may determine the value of an overall sleep quality metric by applying a function or look-up table to a plurality of sleep quality metric values, which may also include the application of weighting factors to one or more of the individual sleep quality metric values.
[0051] In some embodiments, IMD 14 compares a current, a mean, a median, or an overall sleep quality metric value with one or more threshold values, and adjusts the therapy delivered by IMD 14 based on the comparison. In such embodiments, IMD 14 may adjust the intensity of the therapy based on the comparison. For example, IMD 14 may increase the intensity of the therapy when the comparison indicates that the sleep quality experienced by patient 12 is poor in order to address the symptoms which are disturbing the patient's sleep, and/or to address the increase in symptoms which may result from the disturbed sleep.
[0052] For example, in embodiments such that illustrated by FIG. 1 in which IMD 14 is a neurostimulator, IMD 14 may increase the pulse amplitude, pulse width, pulse rate, or duty cycle, e.g., duration, of delivered neurostimulation. As another example, in embodiments in which an IMD is an implantable pump, the IMD may increase the dosage, infusion rate, or duty cycle of a therapeutic agent delivered by the pump. IMD 14 may adjust such parameters within ranges specified by a clinician or a manufacturer of IMD 14.
[0053] In some embodiments, IMD 14 may iteratively and incrementally increase the intensity so long as the comparison indicates poor sleep quality. In other embodiments, IMD 14 may substantially increase the intensity of the therapy when the comparison indicates poor sleep quality in order to more quickly identify an efficacious operating point. In some embodiments, IMD 14 may gradually decrease the intensity of the therapy so long as the comparison indicates that the sleep quality experienced by patient 12 is adequate to, for example, conserve the energy stored by a battery of IMD 14. In other embodiments, the amount by which IMD 14 increases or decreases the intensity of therapy may be proportional to the difference or ratio between the current sleep quality metric value and the threshold value.
[0054] In some embodiments, IMD 14 may identify the current therapy parameter set when a value of one or more sleep quality metrics is collected, and may associate that value with the current therapy parameter set. For example, for each of a plurality of therapy parameter sets, IMD 14 may store a representative value of each of one or more sleep quality metrics in a memory with an indication of the therapy parameter with which that representative value is associated. A representative value of sleep quality metric for a therapy parameter set may be the mean or median of collected sleep quality metric values that have been associated with that therapy parameter set. In some embodiments, IMD 14 controls delivery of therapy according to sleep quality metric values by automatically selecting one of the plurality of therapy parameter sets for use in delivering therapy based on a comparison of their representative sleep quality metric values, e.g., automatically select the therapy parameter set whose representative sleep quality metric values indicate the highest sleep quality.
[0055] FIG. 2 is a block diagram further illustrating system 10. In particular, FIG. 2 illustrates an example configuration of IMD 14 and leads 16A and 16B. FIG. 2 also illustrates sensors 40A and 40B (collectively “sensors 40”) that output signals as a function of one or more physiological parameters of patient 12.
[0056] IMD 14 may deliver neurostimulation therapy via electrodes 42A-D of lead 16A and electrodes 42E-H of lead 16B (collectively “electrodes 42”). Electrodes 42 may be ring electrodes. The configuration, type and number of electrodes 42 illustrated in FIG. 2 are merely exemplary. For example, leads 16A and 16B may each include eight electrodes 42, and the electrodes 42 need not be arranged linearly on each of leads 16A and 16B.
[0057] Electrodes 42 are electrically coupled to a therapy delivery module 44 via leads 16A and 16B. Therapy delivery module 44 may, for example, include an output pulse generator coupled to a power source such as a battery. Therapy delivery module 44 may deliver electrical pulses to patient 12 via at least some of electrodes 42 under the control of a processor 46, which controls therapy delivery module 44 to deliver neurostimulation therapy according to one or more neurostimulation therapy parameter sets selected from available parameter sets stored in a memory 48. However, the invention is not limited to implantable neurostimulator embodiments or even to IMDs that deliver electrical stimulation. For example, in some embodiments a therapy delivery module of an IMD may include a pump, circuitry to control the pump, and a reservoir to store a therapeutic agent for delivery via the pump, and a processor of the IMD may control delivery of a therapeutic agent by the pump according to an infusion parameter set selected from among a plurality of infusion parameter sets stored in a memory.
[0058] IMD 14 may also include a telemetry circuit 50 that enables processor 46 to communicate with programmers 20, 26. Via telemetry circuit 50, processor 46 may receive therapy parameter sets specified by a clinician from clinician programmer 20 for storage in memory 48. Processor 46 may also receive therapy parameter set selections and therapy adjustments made by patient 12 using patient programmer 26 via telemetry circuit 50. In some embodiments, processor 46 may provide diagnostic information recorded by processor 46 and stored in memory 48 to one of programmers 20, 26 via telemetry circuit 50.
[0059] Processor 46 may include a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), discrete logic circuitry, or the like. Memory 48 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, and the like. In some embodiments, memory 48 stores program instructions that, when executed by processor 46, cause IMD 14 and processor 46 to perform the functions attributed to them herein.
[0060] Each of sensors 40 outputs a signal as a function of one or more physiological parameters of patient 12. IMD 14 may include circuitry (not shown) that conditions the signals output by sensors 40 such that they may be analyzed by processor 46. For example, IMD 14 may include one or more analog to digital converters to convert analog signals output by sensors 40 into digital signals usable by processor 46, as well as suitable filter and amplifier circuitry. Although shown as including two sensors 40, system 10 may include any number of sensors.
[0061] Further, as illustrated in FIG. 2 , sensors 40 may be included as part of IMD 14, or coupled to IMD 14 via leads 16. Sensors 40 may be coupled to IMD 14 via therapy leads 16A and 16B, or via other leads 16, such as lead 16C depicted in FIG. 2 . In some embodiments, a sensor located outside of IMD 14 may be in wireless communication with processor 46.
[0062] As discussed above, exemplary physiological parameters of patient 12 that may be monitored by IMD 14 to determine values of one or more sleep quality metrics include activity level, posture, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, core temperature, arterial blood flow, and the level of melatonin within a bodily fluid of patient 12. Further, as discussed above, external medical device embodiments of the invention may additionally or alternatively monitor galvanic skin response. Sensors 40 may be of any type known in the art capable of outputting a signal as a function of one or more of these parameters.
[0063] In some embodiments, in order to determine one or more sleep quality metric values, processor 46 determines when patient 12 is attempting to fall asleep. For example, processor 46 may identify the time that patient begins attempting to fall asleep based on an indication received from patient 12, e.g., via clinician programmer 20 and a telemetry circuit 50. In other embodiments, processor 46 identifies the time that patient 12 begins attempting to fall asleep based on the activity level of patient 12.
[0064] In such embodiments, IMD 14 may include one or more sensors 40 that generate a signal as a function of patient activity. For example, sensors 40 may include one or more accelerometers, gyros, mercury switches, or bonded piezoelectric crystals that generates a signal as a function of patient activity, e.g., body motion, footfalls or other impact events, and the like. Processor 46 may identify a time when the activity level of patient 12 falls below a threshold activity level value stored in memory 48, and may determine whether the activity level remains substantially below the threshold activity level value for a threshold amount of time stored in memory 48. In other words, patient 12 remaining inactive for a sufficient period of time may indicate that patient 12 is attempting to fall asleep. If processor 46 determines that the threshold amount of time is exceeded, processor 46 may identify the time at which the activity level fell below the threshold activity level value as the time that patient 12 began attempting to fall asleep.
[0065] In some embodiments, processor 46 determines whether patient 12 is attempting to fall asleep based on whether patient 12 is or is not recumbent, e.g., lying down. In such embodiments, sensors 40 may include a plurality of accelerometers, gyros, or magnetometers oriented orthogonally that generate signals which indicate the posture of patient 12. In addition to being oriented orthogonally with respect to each other, each of sensors 40 used to detect the posture of patient 12 may be generally aligned with an axis of the body of patient 12. In exemplary embodiments, IMD 14 includes three orthogonally oriented posture sensors 40.
[0066] When sensors 40 include accelerometers, for example, that are aligned in this manner, processor 46 may monitor the magnitude and polarity of DC components of the signals generated by the accelerometers to determine the orientation of patient 12 relative to the Earth's gravity, e.g., the posture of patient 12. In particular, the processor 46 may compare the DC components of the signals to respective threshold values stored in memory 48 to determine whether patient 12 is or is not recumbent. Further information regarding use of orthogonally aligned accelerometers to determine patient posture may be found in a commonly assigned U.S. Pat. No. 5,593,431, which issued to Todd J. Sheldon.
[0067] In some embodiments, processor 46 considers both the posture and the activity level of patient 12 when determining whether patient 12 is attempting to fall asleep. For example, processor 46 may determine whether patient 12 is attempting to fall asleep based on a sufficiently long period of sub-threshold activity, as described above, and may identify the time that patient began attempting to fall asleep as the time when patient 12 became recumbent. Any of a variety of combinations or variations of these techniques may be used to determine when patient 12 is attempting to fall asleep, and a specific one or more techniques may be selected based on the sleeping and activity habits of a particular patient.
[0068] In other embodiments, processor 46 determines when patient 12 is attempting to fall asleep based on the level of melatonin in a bodily fluid. In such embodiments, a sensor 40 may take the form of a chemical sensor that is sensitive to the level of melatonin or a metabolite of melatonin in the bodily fluid, and estimate the time that patient 12 will attempt to fall asleep based on the detection. For example, processor 46 may compare the melatonin level or rate of change in the melatonin level to a threshold level stored in memory 48, and identify the time that threshold value is exceeded. Processor 46 may identify the time that patient 12 is attempting to fall asleep as the time that the threshold is exceeded, or some amount of time after the threshold is exceeded. Any of a variety of combinations or variations of the above-described techniques may be used to determine when patient 12 is attempting to fall asleep, and a specific one or more techniques may be selected based on the sleeping and activity habits of a particular patient.
[0069] Processor 46 may also determine when patient 12 is asleep, e.g., identify the times that patient 12 falls asleep and wakes up, in order to determine one or more sleep quality metric values. The detected values of physiological parameters of patient 12, such as activity level, heart rate, respiration rate, respiratory volume, blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, core temperature, arterial blood flow and galvanic skin response may discernibly change when patient 12 falls asleep or wakes up. In particular, these physiological parameters may be at low values when patient 12 is asleep. Further, the variability of at least some of these parameters, such as heart rate and respiration rate, may be at a low value when the patient is asleep.
[0070] Consequently, in order to detect when patient 12 falls asleep and wakes up, processor 46 may monitor one or more of these physiological parameters, or the variability of these physiological parameters, and detect the discernable changes in their values associated with a transition between a sleeping state and an awake state. In some embodiments, processor 46 may determine a mean or median value for a parameter based on values of a signal over time, and determine whether patient 12 is asleep or awake based on the mean or median value. Processor 46 may compare one or more parameter or parameter variability values to thresholds stored in memory 48 to detect when patient 12 falls asleep or awakes. The thresholds may be absolute values of a physiological parameter, or time rate of change values for the physiological parameter, e.g., to detect sudden changes in the value of a parameter or parameter variability. In some embodiments, a threshold used by processor 46 to determine whether patient 12 is asleep may include a time component. For example, a threshold may require that a physiological parameter be above or below a threshold value for a period of time before processor 46 determines that patient is awake or asleep.
[0071] In some embodiments, in order to determine whether patient 12 is asleep, processor 46 monitors a plurality of physiological parameters, and determines a value of a metric that indicates the probability that patient 12 is asleep for each of the parameters based on a value of the parameter. In particular, the processor 46 may apply a function or look-up table to the current, mean or median value, and/or the variability of each of a plurality of physiological parameters to determine a sleep probability metric value for each of the plurality of physiological parameters. A sleep probability metric value may be a numeric value, and in some embodiments may be a probability value, e.g., a number within the range from 0 to 1, or a percentage level.
[0072] Processor 46 may average or otherwise combine the plurality of sleep probability metric values to provide an overall sleep probability metric value. In some embodiments, processor 46 may apply a weighting factor to one or more of the sleep probability metric values prior to combination. Processor 46 may compare the overall sleep probability metric value to one or more threshold values stored in memory 48 to determine when patient 12 falls asleep or awakes. Use of sleep probability metric values to determine when a patient is asleep based on a plurality of monitored physiological parameters is described in greater detail in a commonly-assigned and copending U.S. patent application Ser. No. 10/825,964, by Ken Heruth and Keith Miesel, entitled “DETECTING SLEEP,” which was filed on Apr. 15, 2004, and is incorporated herein by reference in its entirety.
[0073] To enable processor 46 to determine when patient 12 is asleep or awake, sensors 40 may include, for example, activity sensors as described above. As another example, sensors 40 may include electrodes located on leads or integrated as part of the housing of IMD 14 that output an electrogram signal as a function of electrical activity of the heart of patient 12, and processor 46 may monitor the heart rate of patient 12 based on the electrogram signal. In other embodiments, a sensor may include an acoustic sensor within IMD 14, a pressure sensor within the bloodstream or cerebrospinal fluid of patient 12, or a temperature sensor located within the bloodstream of patient 12. The signals output by such sensors may vary as a function of contraction of the heart of patient 12, and can be used by IMD 14 to monitor the heart rate of patient 12.
[0074] In some embodiments, sensors 40 may include an electrode pair, including one electrode integrated with the housing of IMD 14 and one of electrodes 42, that outputs a signal as a function of the thoracic impedance of patient 12, which varies as a function of respiration by patient 12. In other embodiments, sensors 40 may include a strain gage, bonded piezoelectric element, or pressure sensor within the blood or cerebrospinal fluid that outputs a signal that varies based on patient respiration. An electrogram output by electrodes as discussed above may also be modulated by patient respiration, and may be used as an indirect representation of respiration rate.
[0075] Sensors 40 may include electrodes that output an electromyogram (EMG) signal as a function of muscle electrical activity, or may include any of a variety of known temperature sensors to output a signal as a function of a core temperature of patient 12. Such electrodes and temperature sensors may be incorporated within the housing of IMD 14, or coupled to IMD 14 via leads. Sensors 40 may also include a pressure sensor within, or in contact with, a blood vessel. The pressure sensor may output a signal as a function of the a blood pressure of patient 12, and may, for example, comprise a Chronicle Hemodynamic Monitor™ commercially available from Medtronic, Inc. of Minneapolis, Minn.
[0076] Sensors 40 may also include optical pulse oximetry sensors or Clark dissolved oxygen sensors located within, as part of a housing of, or outside of IMD 14, which output signals as a function of blood oxygen saturation and blood oxygen partial pressure respectively. In some embodiments, system 10 may include a catheter with a distal portion located within the cerebrospinal fluid of patient 12, and the distal end may include a Clark sensor to output a signal as a function of the partial pressure of oxygen within the cerebrospinal fluid. Embodiments in which an IMD comprises an implantable pump, for example, may include a catheter with a distal portion located in the cerebrospinal fluid.
[0077] In some embodiments, sensors 40 may include one or more intraluminal, extraluminal, or external flow sensors positioned to output a signal as a function of arterial blood flow. A flow sensor may be, for example, and electromagnetic, thermal convection, ultrasonic-Doppler, or laser-Doppler flow sensor. Further, in some external medical device embodiments of the invention, sensors 40 may include one or more electrodes positioned on the skin of patient 12 to output a signal as a function of galvanic skin response.
[0078] Processor 46 may also detect arousals and/or apneas that occur when patient 12 is asleep based on one or more of the above-identified physiological parameters. For example, processor 46 may detect an arousal based on an increase or sudden increase in one or more of heart rate, heart rate variability, respiration rate, respiration rate variability, blood pressure, or muscular activity as the occurrence of an arousal. Processor 46 may detect an apnea based on a disturbance in the respiration rate of patient 12, e.g., a period with no respiration. Memory 48 may store thresholds used by processor 46 to detect arousals and apneas. Processor 46 may determine, as a sleep quality metric value, the number of apnea events and/or arousals during a night.
[0079] Further, in some embodiments, processor 46 may determine which sleep state patient 12 is in during sleep, e.g., REM, S1, S2, S3, or S4, based on one or more of the monitored physiological parameters. In particular, memory 48 may store one or more thresholds for each of sleep states, and processor 46 may compare physiological parameter or sleep probability metric values to the thresholds to determine which sleep state patient 12 is currently in. Processor 46 may determine, as sleep quality metric values, the amounts of time per night spent in the various sleep states. As discussed above, inadequate time spent in deeper sleep states, e.g., S3 and S4, is an indicator of poor sleep quality. Consequently, in some embodiments, processor 46 may determine an amount or percentage of time spent in one or both of the S3 and S4 sleep states as a sleep quality metric.
[0080] FIG. 3 further illustrates memory 48 of IMD 14. As illustrated in FIG. 3 , memory 48 stores a plurality of therapy parameter sets 60. Therapy parameter sets 60 may include parameter sets specified by a clinician using clinician programmer 20. Therapy parameter sets 60 may also include parameter sets that are the result of patient 12 changing one or more parameters of one of the preprogrammed therapy parameter sets via patient programmer 26.
[0081] Memory 48 may also include parameter information 60 recorded by processor 46, e.g., physiological parameter values, or mean or median physiological parameter values. Memory 48 stores threshold values 64 used by processor 46 in the collection of sleep quality metric values, as discussed above. In some embodiments, memory 48 also stores one or more functions or look-up tables (not shown) used by processor 46 to determine sleep probability metric values, or to determine an overall sleep quality metric value.
[0082] Further, processor 46 stores determined values 66 for one or more sleep quality metrics within memory 48. Processor 46 may collect sleep quality metric values 66 each time patient 12 sleeps, or only during selected times that patient 12 is asleep. Processor 46 may store each sleep quality metric value determined within memory 48 as a sleep quality metric value 66, or may store mean or median sleep quality metric values over periods of time such as weeks or months as sleep quality metric values 66. Further, processor 46 may apply a function or look-up table to a plurality of sleep quality metric values to determine overall sleep quality metric value, and may store the overall sleep quality metric values within memory 48. The application of a function or look-up table by processor 46 for this purpose may involve the use or weighting factors for one or more of the individual sleep quality metric values.
[0083] In some embodiments, processor 46 identifies which of therapy parameter sets 60 is currently selected for use in delivering therapy to patient 12 when a value of one or more sleep quality metrics is collected, and may associate that value with the current parameter set. For example, for each available therapy parameter set 60, processor 46 may store a representative value of each of one or more sleep quality metrics within memory 48 as a sleep quality metric value 66 with an indication of which therapy parameter set that representative value is associated with. A representative value of sleep quality metric for a therapy parameter set may be the mean or median of collected sleep quality metric values that have been associated with that parameter set.
[0084] In some embodiments, as discussed above, processor 46 may adjust the intensity of the therapy delivered by therapy module 44 based on one or more sleep quality metric values 66. In particular, processor 46 may adjust one or more therapy parameters, such as pulse amplitude, pulse width, pulse rate, and duty cycle to adjust the intensity of the stimulation. In some embodiments, memory 48 may store parameter ranges 68 specified by a clinician or the manufacturer of IMD 14, and processor 46 may adjust parameters within the specified ranges.
[0085] In some embodiments, processor 46 may iteratively and incrementally increase the intensity so long as the comparison indicates poor sleep quality. In other embodiments, processor 46 may substantially increase the intensity of the therapy when the comparison indicates poor sleep quality in order to more quickly identify an efficacious operating point. In some embodiments, processor 46 may gradually decrease the intensity of the therapy so long as the comparison indicates that the sleep quality experienced by patient 12 is adequate to, for example, conserve the energy stored by a battery of IMD 14. In other embodiments, the amount by which processor 46 increases or decreases the intensity of therapy may be proportional to the difference or ratio between the current sleep quality metric value and a threshold value.
[0086] FIG. 4 is a flow diagram illustrating an example method for collecting sleep quality information that may be employed by IMD 14. IMD 14 monitors the posture, activity level, and/or melatonin level of patient 12, or monitors for an indication from patient 12, e.g., via patient programmer 26 (70), and determines whether patient 12 is attempting to fall asleep based on the posture, activity level, melatonin level, and/or a patient indication, as described above (72). If IMD 14 determines that patient 12 is attempting to fall asleep, IMD 14 identifies the time that patient 12 began attempting to fall asleep using any of the techniques described above (74), and monitors one or more of the various physiological parameters of patient 12 discussed above to determine whether patient 12 is asleep (76, 78).
[0087] In some embodiments, IMD 14 compares parameter values or parameter variability values to one or more threshold values 64 to determine whether patient 12 is asleep. In other embodiments, IMD 14 applies one or more functions or look-up tables to determine one or more sleep probability metric values based on the physiological parameter values, and compares the sleep probability metric values to one or more threshold values 64 to determine whether patient 12 is asleep. While monitoring physiological parameters (76) to determine whether patient 12 is asleep (78), IMD 14 may continue to monitor the posture and/or activity level of patient 12 (70) to confirm that patient 12 is still attempting to fall asleep (72).
[0088] When IMD 14 determines that patient 12 is asleep, e.g., by analysis of the various parameters contemplated herein, IMD 14 will identify the time that patient 12 fell asleep (80). While patient 12 is sleeping, IMD 14 will continue to monitor physiological parameters of patient 12 (82). As discussed above, IMD 14 may identify the occurrence of arousals and/or apneas based on the monitored physiological parameters (84). Further, IMD 14 may identify the time that transitions between sleep states, e.g., REM, S1, S2, S3, and S4, occur based on the monitored physiological parameters (84).
[0089] Additionally, while patient 12 is sleeping, IMD 14 monitors physiological parameters of patient 12 (82) to determine whether patient 12 has woken up (86). When IMD 14 determines that patient 12 is awake, IMD 14 identifies the time that patient 12 awoke (88), and determines sleep quality metric values based on the information collected while patient 12 was asleep (90).
[0090] For example, one sleep quality metric value IMD 14 may calculate is sleep efficiency, which IMD 14 may calculate as a percentage of time during which patient 12 is attempting to sleep that patient 12 is actually asleep. IMD 14 may determine a first amount of time between the time IMD 14 identified that patient 12 fell asleep and the time IMD 14 identified that patient 12 awoke. IMD 14 may also determine a second amount of time between the time IMD 14 identified that patient 12 began attempting to fall asleep and the time IMD 14 identified that patient 12 awoke. To calculate the sleep efficiency, IMD 14 may divide the first time by the second time.
[0091] Another sleep quality metric value that IMD 14 may calculate is sleep latency, which IMD 14 may calculate as the amount of time between the time IMD 14 identified that patient 12 was attempting to fall asleep and the time IMD 14 identified that patient 12 fell asleep. Other sleep quality metrics with values determined by IMD 14 based on the information collected by IMD 14 in the illustrated example include: total time sleeping per day, at night, and during daytime hours; number of apnea and arousal events per occurrence of sleep; and amount of time spent in the various sleep states, e.g., one or both of the S3 and S4 sleep states. IMD 14 may store the determined values as sleep quality metric values 66 within memory 48.
[0092] IMD 14 may perform the example method illustrated in FIG. 4 continuously, e.g., may monitor to identify when patient 12 is attempting to sleep and asleep any time of day, each day. In other embodiments, IMD 14 may only perform the method during evening hours and/or once every N days to conserve battery and memory resources. Further, in some embodiments, IMD 14 may only perform the method in response to receiving a command from patient 12 or a clinician via one of programmers 20, 26. For example, patient 12 may direct IMD 14 to collect sleep quality information at times when the patient believes that his or her sleep quality is low or therapy is ineffective.
[0093] FIG. 5 is a flow diagram illustrating an example method for associating sleep quality information with therapy parameter sets 60 that may be employed by IMD 14. IMD 14 determines a value of a sleep quality metric according to any of the techniques described above (100). IMD 14 also identifies the current therapy parameter set, e.g., the therapy parameter set 60 used by IMD 14 to control delivery of therapy when patient 12 was asleep (102), and associates the newly determined value with the current therapy parameter set 60.
[0094] Among sleep quality metric values 66 within memory 48, IMD 14 stores a representative value of the sleep quality metric, e.g., a mean or median value, for each of the plurality of therapy parameter sets 60. IMD 14 updates the representative values for the current therapy parameter set based on the newly determined value of the sleep quality metric. For example, a newly determined sleep efficiency value may be used to determine a new average sleep efficiency value for the current therapy parameter set 60.
[0095] FIG. 6 is a flow diagram illustrating an example method for controlling therapy based on sleep quality information that may be employed by IMD 14. IMD 14 determines a value 66 of a sleep quality metric according to any of the techniques described above (110). The sleep quality metric value 66 may be a current value, mean value, median value, or an overall value, as described above.
[0096] IMD 14 compares the value 66 of the sleep quality metric to one or more threshold values (112), and may determine whether the sleep quality experienced by patient 12 is poor based on the comparison (114). For example, IMD 14 may determine that the sleep quality is poor if the sleep quality metric value 66 falls below a threshold value, or has decreased by greater than a threshold amount over a period of time. In some embodiments, IMD 14 may compare values 66 for a plurality of sleep quality metrics to respective thresholds to determine whether patient 12 is experiencing poor sleep quality.
[0097] If patient 12 is experiencing poor sleep quality, IMD 14 may increase the intensity of therapy, e.g., increase a pulse amplitude, pulse width, pulse rate, duty cycle, dosage, or infusion rate (116). On the other hand, if the sleep quality is adequate, IMD 14 may decrease the intensity of the therapy (118). IMD 14 may adjust the intensity of therapy by adjusting the values of therapy parameters within ranges 68, as discussed above.
[0098] IMD 14 need not increase and decrease the intensity of therapy by the same amount, e.g., at the same rate. For example, IMD 14 may increase therapy intensity at a greater rate than it decreases therapy intensity to provide patient 12 more immediate relief when sleep quality is poor, and to avoid frequent reduction of the therapy intensity below a point at which sleep quality begins to decline. When adjusting the intensity of therapy, IMD 14 may either temporarily or permanently adjust one or more parameters of the currently selected therapy parameter set 60.
[0099] In some embodiments, IMD 14 may iteratively and incrementally increase the intensity so long as the comparison indicates poor sleep quality. In other embodiments, IMD 14 may substantially increase the intensity of the therapy when the comparison indicates poor sleep quality in order to more quickly identify an efficacious operating point. In some embodiments, IMD 14 may gradually decrease the intensity of the therapy so long as the comparison indicates that the sleep quality experienced by patient 12 is adequate to, for example, conserve the energy stored by a battery of IMD 14. In other embodiments, the amount by which IMD 14 increases or decreases the intensity of therapy may be proportional to the difference or ratio between the current sleep quality metric value and the threshold value.
[0100] FIG. 7 is a flow diagram illustrating another example method for controlling therapy based on sleep quality information that may be employed by IMD 14. In particular, FIG. 7 illustrates a method that may be employed by IMD 14 in embodiments in which IMD 14 stores associates sleep quality metric values 66 with therapy parameter sets 60, and stores representative values 66 of the sleep quality metrics for the therapy parameter sets 60. IMD 14 determines a value 66 of a sleep quality metric (120), and compares the value 66 to a threshold 64 (122) to determine whether patient 12 is experiencing poor sleep quality (124), as described above with reference to FIG. 6 .
[0101] If the comparison indicates that the sleep quality experienced by patient 12 is poor, IMD 14 compares the representative values 66 of the sleep quality metrics (126), and automatically selects one of the therapy parameter sets 60 for use in controlling delivery of therapy based on the comparison (128). IMD 14 may, for example, select the therapy parameter set 60 with the “best” representative value or values in order to provide the therapy most likely to improve the quality of the patient's sleep. In some embodiments, IMD 14 may detect subsequent times when patient 12 is sleeping using the techniques described above, and may automatically activate the selected therapy parameter set at those times. IMD 14 may use the selected therapy parameter set in this manner for a specified time period, e.g., a number of days, or until patient 12 overrides the selection via patient programmer 26.
[0102] Various embodiments of the invention have been described. However one skilled in the art will appreciate that various modifications may be made to the described embodiments without departing from the scope of the invention. For example, although described herein primarily in the context of treatment of pain with an implantable neurostimulator or implantable pump, the invention is not so limited. Moreover, the invention is not limited to implantable medical devices. The invention may be embodied in any implantable or external medical device that delivers therapy to treat any ailment of symptom of a patient. These and other embodiments are within the scope of the following claims.
Claims
The invention claimed is:
1. A method comprising:
monitoring at least one physiological parameter of a patient via a medical device that delivers a therapy to the patient, wherein the therapy comprises electrical stimulation;
determining a value of a metric that is indicative of sleep quality based on the at least one physiological parameter;
comparing the sleep quality metric value to a threshold value; and
adjusting a therapy parameter within a specified range based on the comparison, wherein adjusting the therapy parameter comprises adjusting at least one of a pulse amplitude, pulse rate, pulse width or duty cycle of the stimulation.
2. The method of claim 1, wherein monitoring at least one physiological parameter comprises monitoring at least one of activity level, posture, heart rate, respiration rate, respiratory volume, or core temperature.
3. The method of claim 1, wherein monitoring at least one physiological parameter comprises monitoring at least one of blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, arterial blood flow, melatonin level within a bodily fluid, or galvanic skin response.
4. The method of claim 1, wherein the sleep quality metric comprises sleep efficiency, and determining the value of the sleep quality metric comprises:
identifying when the patient is attempting to sleep;
identifying when the patient is asleep; and
determining a percentage of time that the patient is asleep while the patient is attempting to sleep.
5. The method of claim 1, wherein the sleep quality metric comprises sleep latency, and determining the value of the sleep quality metric comprises:
identifying a first time when the patient begins attempting to sleep;
identifying a second time when the patient falls asleep; and
determining an amount of time between the first and second times.
6. The method of claim 1, wherein determining the value of the sleep quality metric comprises:
identifying when the patient is asleep; and
determining an amount of time that the patient is asleep during a period.
7. The method of claim 1, wherein determining the value of the sleep quality metric comprises:
identifying when the patient is asleep; and
identifying at least one of a number of arousal events or a number of apnea events during a period of sleep.
8. The method of claim 1, wherein determining the value of the sleep quality metric comprises:
identifying when the patient is within a sleep state; and
determining an amount of time that the patient was within the sleep state.
9. The method of claim 8, wherein the sleep state comprises at least one of an S3 sleep state or an S4 sleep state.
10. The method of claim 1, wherein determining a value of a metric that is indicative of sleep quality comprises:
determining a value of each of a plurality of sleep quality metrics; and
determining a value of an overall sleep quality metric based on the plurality of sleep quality metric values.
11. The method of claim 10, wherein determining a value of an overall sleep quality metric comprises applying a weighting factor to at least one of the plurality of sleep quality metric values.
12. The method of claim 1, wherein the sleep quality metric value comprises one of a mean or a median sleep quality metric value.
13. The method of claim 1, wherein the therapy comprises neurostimulation.
14. The method of claim 1, wherein the therapy comprises a therapeutic agent, and adjusting the therapy parameter comprises adjusting at least one of a dosage or an infusion rate for the therapeutic agent.
15. The method of claim 1, wherein the medical device delivers the therapy to the patient to treat chronic pain.
16. The method of claim 1, wherein the medical device comprises an implantable medical device.
17. A medical device comprising:
a therapy module to deliver a therapy to a patient, wherein the therapy comprises electrical stimulation;
a memory to store a specified range for a therapy parameter; and
a processor configured to monitor at least one physiological parameter of a patient based on at least one signal received from at least one sensor, determine a value of a metric that is indicative of sleep quality based on the at least one physiological parameter, compare the sleep quality metric value to a threshold value, and adjust the therapy parameter within the specified range based on the comparison, wherein the processor adjusts at least one of a pulse amplitude, pulse rate, pulse width or duty cycle of the stimulation based on the comparison.
18. The medical device of claim 17, wherein the processor monitors at least one of activity level, posture, heart rate, respiration rate, respiratory volume, or core temperature.
19. The medical device of claim 17, wherein the processor monitors at least one of blood pressure, blood oxygen saturation, partial pressure of oxygen within blood, partial pressure of oxygen within cerebrospinal fluid, muscular activity, arterial blood flow, melatonin level within a bodily fluid, or galvanic skin response.
20. The medical device of claim 17, wherein the sleep quality metric comprises sleep efficiency, and the processor identifies when the patient is attempting to sleep, identifies when the patient is asleep, and determines a percentage of time that the patient is asleep while the patient is attempting to sleep as the value of the sleep quality metric.
21. The medical device of claim 17, wherein the sleep quality metric comprises sleep latency, and the processor identifies a first time when the patient begins attempting to sleep, identifies a second time when the patient falls asleep, and determines an amount of time between the first and second times as the value of the sleep quality metric.
22. The medical device of claim 17, wherein the processor identifies when the patient is asleep, and determines an amount of time that the patient is asleep during a period as the value of the sleep quality metric.
23. The medical device of claim 17, wherein the processor identifies when the patient is asleep, and identifies at least one of a number of arousal events or a number of apnea events during a period of sleep as the value of the sleep quality metric.
24. The medical device of claim 17, wherein the processor identifies when the patient is within a sleep state, and determines an amount of time that the patient was within the sleep state as the value of the sleep quality metric.
25. The medical device of claim 24, wherein the sleep state comprises at least one of an S3 sleep state or an S4 sleep state.
26. The medical device of claim 17, wherein the processor determines a value of each of a plurality of sleep quality metrics, and determines a value of an overall sleep quality metric based on the plurality of sleep quality metric values.
27. The medical device of claim 26, wherein the processor applies a weighting factor to at least one of the plurality of sleep quality metric values to determine the value of the overall sleep quality metric.
28. The medical device of claim 17, wherein the sleep quality metric value comprises one of a mean or a median sleep quality metric value.
29. The medical device of claim 17, wherein the therapy module delivers a therapeutic agent, and the processor adjusts at least one of a dosage or an infusion rate for the therapeutic agent based on the comparison.
30. The medical device of claim 17, wherein the medical device comprises an implantable medical device.
31. The medical device of claim 30, wherein the implantable medical device comprises at least one of an implantable neurostimulator or an implantable pump.
32. The medical device of claim 17, wherein the medical device delivers the therapy to the patient to treat chronic pain.
33. A computer-readable medium comprising instructions that cause a programmable processor to:
monitor at least one physiological parameter of a patient via a medical device that delivers a therapy to the patient;
determine a plurality of values of a metric that is indicative of sleep quality over time based on the at least one physiological parameter;
associate each of the determined values of the sleep quality metric with a current therapy parameter set;
for each of the therapy parameter sets, determine a representative value of the sleep quality metric based on the values of the sleep quality metric associated with the therapy parameter set; and
automatically select one of the therapy parameter sets for delivery of the therapy based on the representative sleep quality metric values for the therapy parameter sets.
34. A method comprising:
monitoring at least one physiological parameter of a patient via a medical device that delivers a therapy to the patient;
determining a value of a metric that is indicative of sleep quality based on the at least one physiological parameter;
comparing the sleep quality metric value to a threshold value; and
adjusting the therapy in an amount proportional to at least one of a difference or a ratio between the sleep quality metric value and the threshold value.
35. The method of claim 34, wherein the medical device delivers the therapy to the patient to treat chronic pain.
36. The method of claim 34, wherein the medical device comprises at least one of an implantable neurostimulator or an implantable pump.
37. The method of claim 34, wherein the sleep quality metric comprises at least one of sleep latency or sleep efficiency.
38. A method comprising:
monitoring at least one physiological parameter of a patient via a medical device that delivers a therapy to the patient;
determining a value of a metric that is indicative of sleep quality based on the at least one physiological parameter;
comparing the sleep quality metric value to a threshold value; and
adjusting the therapy based on the comparison,
wherein adjusting the therapy comprises increasing the intensity of the therapy at a first rate and decreasing the intensity of the therapy at a second rate.
39. The method of claim 38, wherein the medical device delivers the therapy to the patient to treat chronic pain.
40. The method of claim 38, wherein the medical device comprises at least one of an implantable neurostimulator or an implantable pump.
41. The method of claim 38, wherein the sleep quality metric comprises at least one of sleep latency or sleep efficiency.
42. A method comprising:
monitoring at least one physiological parameter of a patient via a medical device that delivers a therapy to the patient;
determining a plurality of values of a metric that is indicative of sleep quality over time based on the at least one physiological parameter;
associating each of the determined values of the sleep quality metric with a current therapy parameter set;
for each of a plurality of therapy parameter sets, determining a representative value of the sleep quality metric based on the values of the sleep quality metric associated with the therapy parameter set; and
automatically selecting one of the therapy parameter sets for delivery of the therapy based on the representative sleep quality metric values for the therapy parameter sets.
43. The method of claim 42, wherein the representative value for each of the therapy parameter sets comprises one of a mean value or a median value.
44. The method of claim 42, wherein the medical device delivers the therapy to the patient to treat chronic pain.
45. The method of claim 42, wherein the medical device comprises at least one of an implantable neurostimulator or an implantable pump.
46. The method of claim 42, wherein the sleep quality metric comprises at least one of sleep latency or sleep efficiency.
47. A medical device comprising:
a therapy module to deliver a therapy to a patient; and
a processor to monitor at least one physiological parameter of a patient based on at least one signal received from at least one sensor, determine a value of a metric that is indicative of sleep quality based on the at least one physiological parameter, compare the sleep quality metric value to a threshold value, and adjust the therapy in an amount proportional to at least one of a difference or a ratio between the sleep quality metric value and the threshold value.
48. The medical device of claim 47, wherein the medical device delivers the therapy to the patient to treat chronic pain.
49. The medical device of claim 47, wherein the medical device comprises at least one of an implantable neurostimulator or an implantable pump.
50. The medical device of claim 47, wherein the sleep quality metric comprises at least one of sleep latency or sleep efficiency.
51. A medical device comprising:
a therapy module to deliver a therapy to a patient; and
a processor to monitor at least one physiological parameter of a patient based on at least one signal received from at least one sensor, determine a value of a metric that is indicative of sleep quality based on the at least one physiological parameter, compare the sleep quality metric value to a threshold value, and adjust the therapy based on the comparison,
wherein the processor increases the intensity of the therapy at a first rate and decreases the intensity of the therapy at a second rate.
52. The medical device of claim 51, wherein the medical device delivers the therapy to the patient to treat chronic pain.
53. The medical device of claim 51, wherein the medical device comprises at least one of an implantable neurostimulator or an implantable pump.
54. The medical device of claim 51, wherein the sleep quality metric comprises at least one of sleep latency or sleep efficiency.
55. A medical device comprising:
a therapy module to deliver a therapy to a patient;
a memory to store information identifying a plurality of therapy parameter sets; and
a processor to monitor at least one physiological parameter of a patient based on at least one signal received from at least one sensor, determine a plurality of values of a metric that is indicative of sleep quality over time based on the at least one physiological parameter, associate each of the determined values of the sleep quality metric with a current one of the therapy parameter sets, for each of the therapy parameter sets, determine a representative value of the sleep quality metric based on the values of the sleep quality metric associated with the therapy parameter set, store the representative value of the sleep quality metric in association with the therapy parameter set within the memory, and automatically select one of the therapy parameter sets for delivery of the therapy based on the representative sleep quality metric values for the therapy parameter sets.
56. The medical device of claim 55, wherein the representative value for each therapy parameter set comprises one of a mean value or a median value.
57. The medical device of claim 55, wherein the medical device delivers the therapy to the patient to treat chronic pain.
58. The medical device of claim 55, wherein the medical device comprises at least one of an implantable neurostimulator or an implantable pump.
59. The medical device of claim 55, wherein the sleep quality metric comprises at least one of sleep latency or sleep efficiency.
60. A method comprising:
monitoring at least one physiological parameter of a patient via a medical device that delivers a therapy to the patient, wherein the therapy comprises a therapeutic agent;
determining a value of a metric that is indicative of sleep quality based on the at least one physiological parameter;
comparing the sleep quality metric value to a threshold value; and
adjusting a therapy parameter within a specified range based on the comparison, wherein adjusting the therapy parameter comprises adjusting at least one of a dosage or an infusion rate for the therapeutic agent.
61. A medical device comprising:
a therapy module to deliver a therapy to a patient, wherein the therapy module delivers a therapeutic agent;
a memory to store a specified range for a therapy parameter; and
a processor configured to monitor at least one physiological parameter of a patient based on at least one signal received from at least one sensor, determine a value of a metric that is indicative of sleep quality based on the at least one physiological parameter, compare the sleep quality metric value to a threshold value, and adjust the therapy parameter within the specified range based on the comparison, wherein processor adjusts at least one of a dosage or an infusion rate for the therapeutic agent based on the comparison.
Patent Citations (133)
| Patent | Date | Inventor | Cited By |
|---|---|---|---|
| US4297685(A) | 1981-10-01 | Brainard, II | Applicant |
| US4550736(A) | 1985-11-01 | Broughton et al. | Applicant |
| US4771780(A) | 1988-09-01 | Sholder | Applicant |
| US4776345(A) | 1988-10-01 | Cohen et al. | Applicant |
| US4846195(A) | 1989-07-01 | Alt | Applicant |
| US5040536(A) | 1991-08-01 | Riff | Applicant |
| US5058584(A) | 1991-10-01 | Bourgeois | Applicant |
| US5125412(A) | 1992-06-01 | Thornton | Applicant |
| US5154180(A) | 1992-10-01 | Blanchet et al. | Applicant |
| US5233984(A) | 1993-08-01 | Thompson | Applicant |
| US5275159(A) | 1994-01-01 | Griebel | Applicant |
| US5335657(A) | 1994-08-01 | Terry, Jr. et al. | Applicant |
| US5337758(A) | 1994-08-01 | Moore et al. | Applicant |
| US5342409(A) | 1994-08-01 | Mullett | Applicant |
| US5469861(A) | 1995-11-01 | Piscopo et al. | Applicant |
| US5476483(A) | 1995-12-01 | Bornzin et al. | Applicant |
| US5514162(A) | 1996-05-01 | Bornzin et al. | Applicant |
| US5593431(A) | 1997-01-01 | Sheldon | Applicant |
| US5622428(A) | 1997-04-01 | Bonnet | Applicant |
| US5645053(A) | 1997-07-01 | Remmers et al. | Examiner |
| US5732696(A) | 1998-03-01 | Rapoport et al. | Applicant |
| US5782884(A) | 1998-07-01 | Stotts et al. | Applicant |
| US5895371(A) | 1999-04-01 | Levitas et al. | Applicant |
| US5904708(A) | 1999-05-01 | Goedeke | Applicant |
| US5919149(A) | 1999-07-01 | Allum | Applicant |
| US5941906(A) | 1999-08-01 | Barreras, Sr. et al. | Applicant |
| US5944680(A) | 1999-08-01 | Christopherson et al. | Applicant |
| US6044297(A) | 2000-03-01 | Sheldon et al. | Applicant |
| US6045513(A) | 2000-04-01 | Stone et al. | Applicant |
| US6059576(A) | 2000-05-01 | Brann | Applicant |
| US6095991(A) | 2000-08-01 | Krausman et al. | Applicant |
| US6102874(A) | 2000-08-01 | Stone et al. | Applicant |
| US6120467(A) | 2000-09-01 | Schallhorn | Applicant |
| US6128534(A) | 2000-10-01 | Park et al. | Applicant |
| US6157857(A) | 2000-12-01 | Dimpfel | Applicant |
| US6165143(A) | 2000-12-01 | van Lummel | Applicant |
| US6259948(B1) | 2001-07-01 | Florio et al. | Applicant |
| US6280409(B1) | 2001-08-01 | Stone et al. | Applicant |
| US6296606(B1) | 2001-10-01 | Goldberg et al. | Applicant |
| US6308098(B1) | 2001-10-01 | Meyer | Applicant |
| US6351672(B1) | 2002-02-01 | Park et al. | Applicant |
| US6366813(B1) | 2002-04-01 | DiLorenzo | Applicant |
| US6416471(B1) | 2002-07-01 | Kumar et al. | Applicant |
| US6440090(B1) | 2002-08-01 | Schallhorn | Applicant |
| US6449508(B1) | 2002-09-01 | Sheldon et al. | Applicant |
| US6459934(B1) | 2002-10-01 | Kadhiresan | Applicant |
| US6466821(B1) | 2002-10-01 | Pianca et al. | Applicant |
| US6468234(B1) | 2002-10-01 | Van der Loos et al. | Applicant |
| US6514218(B2) | 2003-02-01 | Yamamoto | Applicant |
| US6539249(B1) | 2003-03-01 | Kadhiresan et al. | Applicant |
| US6574507(B1) | 2003-06-01 | Bonnet | Applicant |
| US6605038(B1) | 2003-08-01 | Teller et al. | Applicant |
| US6611783(B2) | 2003-08-01 | Kelly, Jr. et al. | Applicant |
| US6659968(B1) | 2003-12-01 | McClure | Applicant |
| US6687538(B1) | 2004-02-01 | Hrdlicka et al. | Applicant |
| US6731984(B2) | 2004-05-01 | Chu et al. | Applicant |
| US6752766(B2) | 2004-06-01 | Kowallik et al. | Applicant |
| US6773404(B2) | 2004-08-01 | Poezevera et al. | Examiner |
| US6819956(B2) | 2004-11-01 | DiLorenzo | Applicant |
| US6878121(B2) | 2005-04-01 | Krausman et al. | Applicant |
| US6881192(B1) | 2005-04-01 | Park | Examiner |
| US6884596(B2) | 2005-04-01 | Civelli et al. | Examiner |
| US6890306(B2) | 2005-05-01 | Poezevera | Applicant |
| US6928324(B2) | 2005-08-01 | Park et al. | Examiner |
| US6964641(B2) | 2005-11-01 | Cho et al. | Applicant |
| US7146211(B2) | 2006-12-01 | Frei et al. | Examiner |
| US7162304(B1) | 2007-01-01 | Bradley | Applicant |
| US7209787(B2) | 2007-04-01 | DiLorenzo | Applicant |
| US2001/0037067(A1) | 2001-11-01 | Tchou et al. | Applicant |
| US2002/0077562(A1) | 2002-06-01 | Kalgren et al. | Applicant |
| US2002/0091308(A1) | 2002-07-01 | Kipshidze et al. | Applicant |
| US2002/0161412(A1) | 2002-10-01 | Sun et al. | Applicant |
| US2002/0169485(A1) | 2002-11-01 | Pless et al. | Applicant |
| US2002/0177882(A1) | 2002-11-01 | DiLorenzo | Applicant |
| US2002/0193697(A1) | 2002-12-01 | Cho et al. | Applicant |
| US2002/0193839(A1) | 2002-12-01 | Cho et al. | Applicant |
| US2003/0004423(A1) | 2003-01-01 | Lavie et al. | Applicant |
| US2003/0139692(A1) | 2003-07-01 | Barrey et al. | Applicant |
| US2003/0149457(A1) | 2003-08-01 | Tcheng et al. | Applicant |
| US2003/0153953(A1) | 2003-08-01 | Park et al. | Applicant |
| US2003/0153955(A1) | 2003-08-01 | Park et al. | Applicant |
| US2003/0153956(A1) | 2003-08-01 | Park et al. | Applicant |
| US2003/0163059(A1) | 2003-08-01 | Poezevera et al. | Applicant |
| US2003/0171791(A1) | 2003-09-01 | KenKnight et al. | Applicant |
| US2003/0212445(A1) | 2003-11-01 | Weinberg | Applicant |
| US2004/0002741(A1) | 2004-01-01 | Weinberg | Applicant |
| US2004/0002742(A1) | 2004-01-01 | Florio | Applicant |
| US2004/0015103(A1) | 2004-01-01 | Aminian et al. | Applicant |
| US2004/0049132(A1) | 2004-03-01 | Barron et al. | Applicant |
| US2004/0102814(A1) | 2004-05-01 | Sorensen et al. | Applicant |
| US2004/0111040(A1) | 2004-06-01 | Ni et al. | Applicant |
| US2004/0111041(A1) | 2004-06-01 | Ni et al. | Applicant |
| US2005/0021103(A1) | 2005-01-01 | DiLorenzo | Applicant |
| US2005/0021104(A1) | 2005-01-01 | DiLorenzo | Applicant |
| US2005/0042589(A1) | 2005-02-01 | Hatlestad et al. | Examiner |
| US2005/0113710(A1) | 2005-05-01 | Stahmann et al. | Applicant |
| US2005/0119703(A1) | 2005-06-01 | DiLorenzo | Applicant |
| US2005/0177192(A1) | 2005-08-01 | Rezai et al. | Examiner |
| US2005/0215847(A1) | 2005-09-01 | Heruth et al. | Applicant |
| US2005/0222626(A1) | 2005-10-01 | DiLorenzo | Applicant |
| US2005/0240086(A1) | 2005-10-01 | Akay | Applicant |
| US2005/0240242(A1) | 2005-10-01 | DiLorenzo | Applicant |
| US2005/0245790(A1) | 2005-11-01 | Bergfalk et al. | Applicant |
| US2006/0224191(A1) | 2006-10-01 | DiLorenzo | Applicant |
| US2006/0293720(A1) | 2006-12-01 | DiLorenzo | Applicant |
| US2007/0073355(A1) | 2007-03-01 | DiLorenzo | Applicant |
| US2007/0142862(A1) | 2007-06-01 | DiLorenzo | Applicant |
| DE19831109 | 2000-01-01 | Applicant | |
| DE10024103 | 2001-11-01 | Applicant | |
| EP564803(A1) | 1993-10-01 | Applicant | |
| EP849715(B1) | 1998-06-01 | Applicant | |
| EP1195139(A1) | 2002-04-01 | Applicant | |
| EP1291036(A2) | 2003-03-01 | Applicant | |
| EP1308182(A2) | 2003-05-01 | Applicant | |
| EP1437159(A1) | 2004-07-01 | Applicant | |
| EP1322227(B1) | 2005-12-01 | Applicant | |
| GB2330912 | 1999-05-01 | Applicant | |
| WO98/00197 | 1998-01-01 | Applicant | |
| WO99/13765 | 1999-03-01 | Applicant | |
| WO1/37930 | 2001-05-01 | Applicant | |
| WO2/28282 | 2002-04-01 | Applicant | |
| WO2/28282(A1) | 2002-04-01 | Applicant | |
| WO2/41771(A1) | 2002-05-01 | Applicant | |
| WO2/087433 | 2002-11-01 | Applicant | |
| WO2/096512(A1) | 2002-12-01 | Applicant | |
| WO2/100267 | 2002-12-01 | Applicant | |
| WO2/100267(A1) | 2002-12-01 | Applicant | |
| WO3/024325 | 2003-03-01 | Applicant | |
| WO3/051356 | 2003-06-01 | Applicant | |
| WO3/051356(A1) | 2003-06-01 | Applicant | |
| WO3/065891(A2) | 2003-08-01 | Applicant | |
| WO2005/028029 | 2005-03-01 | Applicant | |
| WO2005/035050 | 2005-04-01 | Applicant |
Non-Patent Literature (42)
- International Search Report and the Written Opinion of the International Searching Authority for PCT/US2005/008790, dated Jun. 21, 2005 (10 pgs.).Applicant
- M. T. Smith et al, “Presleep Cognitions in Patients with Insomnia Secondary to Chronic Pain”, Journal of Behavioral Medicine, vol. 24, No. 1, 2001.Applicant
- Michael T. Smith et al., “How do sleep disturbance and chronic pain inter-relate? Insights from the longitudinal and cognitive-behavioral clinical trials literature”, Sleep Medicines Reviews, YSMRV 286-Jun. 19, 2003.Applicant
- Suanne Goodrich et al. “The Predictions of Pain Using Measures of Sleep Quality”, Pain Digest (1998) 8:23-25.Applicant
- “Analysis of heart rate dynamics by methods derived from nonlinear mathematics: Clinical applicability and prognostic significance” http://herkules.oulu.fi.isbn9514250133/html/x222.html (2004).Applicant
- U.S. Patent Application entitled “Detecting Sleep”, U.S. Appl. No. 10/825,964, filed on Apr. 15, 2004.Applicant
- U.S. Patent Application entitled “Collecting Posture Information to Evaluate Therapy”, U.S. Appl. No. 10/826,926, filed on Apr. 15, 2004.Applicant
- U.S. Patent Application entitled “Collecting Activity Information to Evaluate Therapy”, U.S. Appl. No. 10/825,965, filed on Apr. 15, 2004.Applicant
- U.S. Patent Application entitled “Collecting Activity and Sleep Quality Information via a Medical Device”, U.S. Appl. No. 10/825,955, filed on Apr. 15, 2004.Applicant
- U.S. Patent Application entitled “Collecting Sleep Quality Information via a Medical Device”, U.S. Appl. No. 10/826,925, filed on Apr. 15, 2004.Applicant
- U.S. Patent Application entitled “Sensitivity Analysis for Selecting Therapy Parameter Sets”, U.S. Appl. No. 11/081,873, filed on Mar. 16, 2005.Applicant
- Kassam, M., “2005 EDP Topic ‘MK4’: Tremor Data-Logger for Parkinson's Disease Patients,” http://www.ee.ryerson.ca/˜courses/edp2005/MK4.html, 3 pgs., (2006).Applicant
- “Design Competition: Runners-Up for the Best Three Designs,” EPN, vol. 26, No. 1, (2002).Applicant
- “MiniMitter® Physiological and Behavioral Monitoring for Humans and Animals,” http://www.minimitter.com/Products/Actiwatch, 3 pgs., (2006).Applicant
- Notification of Transmittal of the International Preliminary Report on Patentability for corresponding PCT Application Serial No. PCT/US2005/008790, dated Mar. 27, 2006, 10 pgs.Applicant
- Amzica, “Physiology of Sleep and Wakefulness as it Relates to the Physiology of Epilepsy,” Journal of Clinical Neurophysiology, American Clinical Neurophysiology Society, 19(6), pp. 488-503, (2002).Applicant
- Dinner, “Effect of Sleep on Epilepsy,” Journal of Clinical Neurophysiology, American Clinical Neurophysiology Society, 19(6), pp. 504-513, (2002).Applicant
- Foldvary-Schaefer, “Sleep Complaints and Epilepsy: The Role of Seizures, Antiepileptic Drugs and Sleep Disorders,” Journal of Clinical Neurophysiology, American Clinical Neurophysiology Society, 19(6), pp. 514-521, (2002).Applicant
- Mendez et al. “Interactions Between Sleep and Epilepsy,” Journal of Clinical Neurophysiology, American Clinical Neurophysiology Society, 18(2), pp. 106-127, (2001).Applicant
- Kerr et al., “Analysis of the sit-stand-sit movement cycle in normal subjects,” Clinical Biomechanics, vol. 12, No. 4, pp. 236-245, (1997).Applicant
- Aminian et al. “Physical Activity Monitoring Based on Accelerometry: Validation and Comparison with Video Observation,” Medical & Biological Engineering & Computing, vol. 37, No. 2, pp. 304-308 (1999).Applicant
- Medcare—A Global Leader in Sleep Diagnostics, Embletta Recording System, http://www.medcare.com/products/diagnostic/embletta/, 2 pgs. Jan. 31, 2005.Applicant
- Medcare—A Global Leader in Sleep Diagnostics, Somnologica for Embletta, http://www.medcare.com/products/diagnostic/embletta/SomnoEmbletta/index.asp, 1 pg. Jan. 31, 2005.Applicant
- MAP Medizin-Technologie GmbH, Poly-MESAM®, http://195.244.124.130/map/de/eng/map—med.nsf/cmsall70564A3FCBE4188AC1256EF4.., 4 pgs. Jan. 31, 2005.Applicant
- Merlin, http://www.aha.ru/˜pir/english/merlin/, 4 pgs. Jan. 31, 2005.Applicant
- Sleep Solutions—PR Newswire: Sleep Solutions Introduces NovaSom™ QSG™ for PSG.., http://www.sleep-solutions.com/press—room/novasom.htm, 2 pgs. Jan. 31, 2005.Applicant
- Itamar Medical Information, http://itamar-medical.com/content.asp?id-id=31, 2 pgs. Jan. 31, 2005.Applicant
- Criticare System Inc.,-504DX Portable Pulse Oximeter, http://www.csiusa.com/504dx.html, 4 pgs. Jan. 31, 2005.Applicant
- Snap® Laboratories, Product Fact Sheet, http://www.snaplab.com/mp—fact.htm, 2 pgs. Jan. 31, 2005.Applicant
- Sleep Strip & Bite Strip, http://www.quietsleep.com/snoringgapnea/sleepstrip.htm, 8 pgs. Jan. 31, 2005.Applicant
- “Bitestrip Flier,” downloaded from Internet Archive of www.quietsleep.com dated Jan. 29, 2005 http://web.archive.org/web/20041124080003/www.quietsleep.com/pdf/Bitestrip+Flier.pdf.Applicant
- “Bilateral Comparisons of the BiteStrip Bruxism Device and Masseter EMG Bruxism Events” downloaded from Internet Archive of www.quietsleep.com dated Jan. 29, 2005 http://web.archive.org/web/20041124075114/www.quietsleep.com/pdf/Bilateral+Comparisons.pdf.Applicant
- “The BiteStrip: A Novel Screener for Sleep Bruxism,” downloaded from Internet Archive of www.quietsleep.com dated Jan. 29, 2005 http://web.archive.org/web/20041124072922/www.quietsleep.com/pdf/BiteStrip-+Novel+Screener.pdf.Applicant
- Office Action dated Jul. 20, 2006 for U.S. Appl. No. 10/825,955, filed Apr. 15, 2004, (10 pgs.).Applicant
- Responsive Amendment dated Oct. 20, 2006 for U.S. Appl. No. 10/825,955 filed April 15, 2004 (21 pgs.).Applicant
- Office Action dated Jan. 17, 2007 for U.S. Appl. No. 10/825,955, filed Apr. 15, 2004, (10 pgs.).Applicant
- Responsive Amendment dated Mar. 19, 2007 for U.S. Appl. No. 10/825,955 filed Apr. 15, 2004 (13 pgs.).Applicant
- Office Action dated Jun. 27, 2007 for U.S. Appl. No. 10/825,955, filed Apr. 15, 2004, (13 pgs.).Applicant
- Office Action dated Jul. 3, 2007 for U.S. Appl. No. 10/826,925, filed Apr. 15, 2004, (22 pgs.).Applicant
- “Watch,” Wikipedia, the free encyclopedia, 6 pgs, http://en.wikipedia.org/wiki/Watch, (2006).Applicant
- “IBM & Citizen Watch develop Linux-based ‘WatchPad’,” 5 pgs., http://www.linuxdevices.com/news/NS6580187845.html, (2006).Applicant
- Tuisku, Katinka, “Motor Activity Measured By Actometry in Neuropsychiatric Disorders,” Department of Psychiatry, University of Helsinski, Helsinki, Finland, 115 pgs., (2002).Applicant