Physiological parameter confidence measure

[0001] The present application is a continuation of U.S. application Ser. No. 14/218,242, filed Mar. 18, 2014, entitled “Physiological Parameter Confidence Measure,” which is a continuation of U.S. application Ser. No. 13/153,349, filed Jun. 3, 2011, entitled “Physiological Parameter Confidence Measure,” which is a continuation of U.S. application Ser. No. 11/367,034, filed Mar. 1, 2006, entitled “Physiological Parameter Confidence Measure,” which claims priority benefit under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 60/657,596, filed Mar. 1, 2005, entitled “Multiple Wavelength Sensor,” No. 60/657,281, filed Mar. 1, 2005, entitled “Physiological Parameter Confidence Measure,” No. 60/657,268, filed Mar. 1, 2005, entitled “Configurable Physiological Measurement System,” and No. 60/657,759, filed Mar. 1, 2005, entitled “Noninvasive Multi-Parameter Patient Monitor.” The present application incorporates the foregoing disclosures herein by reference.

[0002] The present application is related to the following copending U.S. utility applications:

[0003]

		App.
		Ser. No.	Filing Date	Title
	1	12/422,915	Apr. 13, 2009	Multiple Wavelength
				Sensor Emitters
	2	12/839,326	Jul. 19, 2010	Multiple Wavelength
				Sensor Substrate
	3	12/568,469	Sep. 28, 2009	Multiple Wavelength
				Sensor Emitters
	4	11/366,997	Mar. 1, 2006	Multiple Wavelength
				Sensor Drivers
	5	12/782,581	May 18, 2010	Configurable
				Physiological
				Measurement System
	6	11/367,033	Mar. 1, 2006	Noninvasive Multi-
				Parameter Patient
				Monitor
	7	11/367,014	Mar. 1, 2006	Noninvasive Multi-
				Parameter Patient
				Monitor
	8	11/366,208	Mar. 1, 2006	Noninvasive Multi-
				Parameter Patient
				Monitor
	9	12/056,179	Mar. 26, 2008	Multiple Wavelength
				Optical Sensor
	10	12/949,271	Nov. 18, 2010	Physiological
				Measurement System
				With Automatic
				Wavelength
				Adjustment

The present application incorporates the foregoing disclosures herein by reference.

[0004] Spectroscopy is a common technique for measuring the concentration of organic and some inorganic constituents of a solution. The theoretical basis of this technique is the Beer-Lambert law, which states that the concentration c_i of an absorbent in solution can be determined by the intensity of light transmitted through the solution, knowing the pathlength d_λ, the intensity of the incident light I_0,λ, and the extinction coefficient ε_i,λ at a particular wavelength λ. In generalized form, the Beer-Lambert law is expressed as:

[0005] $\begin{array}{cc}{I}_{\lambda }=I_{0,\lambda }{e}^{-d_{\lambda }·{\mu }_{a,\lambda }}& \left(1\right)\\ {\mu }_{a,\lambda }=\sum _{i=1}^n{\varepsilon }_{i,\lambda }·c_i& \left(2\right)\end{array}$
where μ_a,λ is the bulk absorption coefficient and represents the probability of absorption per unit length. The minimum number of discrete wavelengths that are required to solve EQS. 1-2 are the number of significant absorbers that are present in the solution.

[0006] A practical application of this technique is pulse oximetry, which utilizes a noninvasive sensor to measure oxygen saturation (SpO₂) and pulse rate. In general, the sensor has light emitting diodes (LEDs) that transmit optical radiation of red and infrared wavelengths into a tissue site and a detector that responds to the intensity of the optical radiation after absorption (e.g., by transmission or transreflectance) by pulsatile arterial blood flowing within the tissue site. Based on this response, a processor determines measurements for SpO₂, pulse rate, and can output representative plethysmographic waveforms. Thus, “pulse oximetry” as used herein encompasses its broad ordinary meaning known to one of skill in the art, which includes at least those noninvasive procedures for measuring parameters of circulating blood through spectroscopy. Moreover, “plethysmograph” as used herein (commonly referred to as “photoplethysmograph”), encompasses its broad ordinary meaning known to one of skill in the art, which includes at least data representative of a change in the absorption of particular wavelengths of light as a function of the changes in body tissue resulting from pulsing blood.

[0007] Pulse oximeters capable of reading through motion induced noise are available from Masimo Corporation (“Masimo”) of Irvine, Calif. Moreover, portable and other oximeters capable of reading through motion induced noise are disclosed in at least U.S. Pat. Nos. 6,770,028, 6,658,276, 6,157,850, 6,002,952 5,769,785, and 5,758,644, which are owned by Masimo and are incorporated by reference herein. Such reading through motion oximeters have gained rapid acceptance in a wide variety of medical applications, including surgical wards, intensive care and neonatal units, general wards, home care, physical training, and virtually all types of monitoring scenarios.

[0008] FIG. 1 illustrates HbO₂ and Hb absorption μ_a versus wavelength. At red and near IR wavelengths below 970 nm, where water has a significant peak, Hb and HbO₂ are the only significant absorbers normally present in the blood. Thus, typically only two wavelengths are needed to resolve the concentrations of Hb and HbO₂, e.g. a red (RD) wavelength at 660 nm and an infrared (IR) wavelength at 940 nm. In particular, SpO₂ is computed based upon a red ratio Red_AC/Red_DC and an IR ratio IR_AC/IR_DC, which are the AC detector response magnitude at a particular wavelength normalized by the DC detector response at that wavelength. The normalization by the DC detector response reduces measurement sensitivity to variations in tissue thickness, emitter intensity and detector sensitivity, for example. The AC detector response is a plethysmograph, as described above. Thus, the red and IR ratios can be denoted as NP_RD and NP_IR respectively, where NP stands for “normalized plethysmograph.” In pulse oximetry, oxygen saturation is calculated from the ratio NP_RD/NP_IR.

[0009] A multiple wavelength sensor and a noninvasive multi-parameter patient monitor, such as referenced above, make blood absorption measurements at more than a red wavelength and an IR wavelength. In one embodiment, described below, blood absorption measurements are made at eight wavelengths. Advantageously, this rich wavelength data, compared with conventional pulse oximetry, allows a determination of a tissue profile or tissue characterization over a wavelength spectrum.

[0010] FIG. 2 illustrates an example of a “tissue profile” 200 for SpO2=97%. For this example, including FIGS. 3-4, below, the sensor emits eight wavelengths (610, 620, 630, 655, 700, 720, 800 and 905 nm). The graph is a plot of NP ratios 210 versus wavelength 220, where the NP ratios are of the form NP_λ1/NP_λ2. This is a generalization to multiple wavelengths of the ratio NP_RD/NP_IR described above for two (red and IR) wavelengths. In order to provide a common scale for these NP ratios, the ratios are calculated with respect to a reference wavelength, λr, which may be any of the available wavelengths. Thus, the plotted NP ratios are denoted NP_λn/NP_λr over the n available wavelengths, including λr. Note that the NP ratio at the reference wavelength is NP_λr/NP_λr=1, which is 800 nm in FIG. 2.

[0011] As shown in FIG. 2, when a sensor is properly positioned on a tissue site, the detector only receives LED emitted light that has propagated through the tissue site after tissue scattering and absorption. Thus, a tissue profile 200 should reflect the blood constituent absorption characteristics illustrated in FIG. 1, above. For this high oxygen saturation (97%) example, HbO₂ is the only significantly absorbing blood constituent and, indeed, the resulting tissue profile 200 is shaped like the HbO₂ absorption curve 110 (FIG. 1).

[0023] In this application, reference is made to many blood parameters. Some references that have common shorthand designations are referenced through such shorthand designations. For example, as used herein, HbCO designates carboxyhemoglobin, HbMet designates methemoglobin, and Hbt designates total hemoglobin. Other shorthand designations such as COHb, MetHb, and tHb are also common in the art for these same constituents. These constituents are generally reported in terms of a percentage, often referred to as saturation, relative concentration or fractional saturation. Total hemoglobin is generally reported as a concentration in g/dL. The use of the particular shorthand designators presented in this application does not restrict the term to any particular manner in which the designated constituent is reported.

[0024] FIG. 3 illustrates an example of a probe-off profile 300. When a sensor is completely dislodged from a patient, a so-called “probe off” condition occurs. Despite a probe off condition, an optical sensor may continue to detect an AC signal, which can be induced at the detector by other than pulsatile arterial absorption of LED emitted light. For example, small patient movements, vibrations, air flow or other perturbations may cause the pathlength between the LEDs and the detector to vary, resulting in an AC detector signal that can be mistakenly interpreted by the monitor as due to pulsatile arterial blood. Further, ambient light may reach the detector, and any modulation of the ambient light due to AC power, power fluctuations, moving objects, such as a fan, among other perturbations can be also mistaken as a pulsatile arterial signal. Probe off errors are serious because a blood constituent monitor may display normal results, such as oxygen saturation, when, in fact, the sensor is not properly attached to the patient, potentially leading to missed severe desaturation events. As shown in FIG. 3, a probe-off profile 300 is readily apparent as it does not have a shape related to the absorption characteristics of hemoglobin constituents.

[0025] FIG. 4 illustrates an example of a penumbra profile 400. When a sensor is not properly positioned or becomes partially dislodged, a penumbra condition may occur, where the detector is “shadowed” by a tissue site, such as a finger, but also receives some light directly from the emitters or indirectly reflected off the sensor housing, or both. As a result, the DC signal at the detector rises significantly, which lowers the AC/DC ratio (NP). Because red wavelengths are more significantly absorbed by Hb and HbO2, the penumbra condition is most noticeable at the red portion 405 of the NP_λn/NP_λr. This effect is readily seen in the penumbra profile 400 as compared to a normal tissue profile 200 (FIG. 2).

[0026] Advantageously, a physiological parameter confidence measurement system, as described below, can distinguish a tissue profile 200 (FIG. 2) from a probe-off profile 300 (FIG. 3) or penumbra profile 400 (FIG. 4), as examples. Further, a physiological parameter confidence measurement system can provide indications that the detector signal is degraded as the result of various physiological and non-physiological phenomenon.

[0027] FIG. 5 illustrates a physiological parameter confidence measurement system 500 having a physiological data 510 input, a confidence indicator 560 output and a probe-off indicator 570 output. In one embodiment, physiological data 510, such as the NP ratios described above, is derived from a sensor 501 generating a sensor signal 502 responsive to multiple wavelengths of optical radiation transmitted into and attenuated by a tissue site. The confidence indicator 560 provides an observer with some measure of “goodness” for the physiological data 510. That is, if confidence is high, it is likely the physiological data 510 is representative of a physiological condition or state. If confidence is low, the physiological data 510 may be less representative of a physiological condition or state. If the confidence is very low, a probe-off indicator 570 may be generated to alert an observer to the possibility that a sensor from which the physiological data 510 is derived is not properly positioned on a tissue site and may not be generating physiologically significant data. In one embodiment, a confidence measure may be provided as a percentage, such as 0-100%. In various embodiments, a confidence indicator 560 corresponding to a confidence measure may be visual or audible or both. For example, a confidence indicator 560 may be a number display, a display message, a bar display, a color indicator or display, such as green (high confidence), yellow (average confidence), red (low confidence). Also, a confidence indicator 560 may be any of various alarm sounds, tones or patterns of sounds or tones, such as a double beep at less than high confidence. In one embodiment, the physiological parameter confidence measurement system 500 is incorporated within a physiological monitor 503 having a display 580 or alarm 590 for outputting the confidence indicator 560 or probe-off indicator 570.

[0028] As shown in FIG. 5, the physiological parameter confidence measurement system 500 also has a parameter estimator 520, a physiological data reference 540 and a confidence measurer 550. The parameter estimator 520 derives one or more physiological parameter estimates, {circumflex over (P)}, 530 based upon the physiological data 510. The parameter estimate or estimates 530 are used to select one or more data clusters 545 from the physiological data reference 540. In one embodiment, the physiological data reference 540 is a collection of predetermined physiological data organized in data clusters. For example the physiological data reference 540 may contain clinically-derived physiological data organized according to corresponding values of a physiological parameter determined by a “gold standard” instrument. In a particular embodiment, the physiological data are NP ratios obtained for various physiological parameters, such as SpO₂, HbCO, HbMet, Hbt, fractional oxygen saturation, bilirubin or glucose to name a few, as measured with a standardized cooximeter, for example. In one embodiment, the physiological data reference 540 is a non-volatile memory or other data storage device containing predetermined physiological data. The confidence measurer 550 uses the physiological data 510 and the selected data cluster or data clusters 545 to generate the confidence indicator 560, the probe-off indicator 570 or both.

[0029] A confidence measurement and confidence indicator, as described herein, may be combined with other signal quality and data confidence measurements and indicators, such as those described in U.S. Pat. No. 6,996,427 titled Pulse Oximetry Data Confidence Indicator and U.S. Pat. No. 6,606,511 titled Pulse Oximetry Pulse Indicator, both patents assigned to Masimo Corporation, Irvine, Calif. and incorporated by reference herein. A probe off measurement and probe off indicator as described herein may be combined with other probe off measurements and indicators, such as those described in U.S. Pat. No. 6,654,624 titled Pulse Oximeter Probe-Off Detector and U.S. Pat. No. 6,771,994 titled Pulse Oximeter Probe-Off Detection System, both patents assigned to Masimo Corporation, Irvine, Calif. and incorporated by reference herein.

[0030] FIG. 6 illustrates NP ratio versus wavelength curves computed from a multiple wavelength sensor, such as described in the U.S. patent application titled Multiple Wavelength Sensor, referenced above. In this example, the sensor emits eight wavelengths (620, 630, 660, 700, 730, 805, 905 and 960 nm). Shown is a low oxygen saturation curve 610, e.g. SpO₂=70% and a high oxygen saturation curve 620, e.g. SpO₂≈100%. By comparison, a conventional two wavelength pulse oximetry sensor, as described above, results in a single point on a particular curve. Advantageously, the NP ratio curves 610, 620 represent a tissue profile that can be compared to a particular sensor response to determine if a physiologically significant measurement has been made. In one embodiment, the NP ratio curves 610, 620 delineate the boundaries of a physiologically significant NP ratio region 630. Although described above with respect to SpO₂, such regions or boundaries can be derived for other physiological parameters such as HbCO, HbMet, Hbt, fractional oxygen saturation, bilirubin or glucose to name a few.

[0031] FIG. 7 illustrates one embodiment of a physiological parameter confidence measurement system 700 utilizing a NP ratio region such as described with respect to FIG. 6, above. The confidence measurement system 700 has input NP ratios 710 measured in response to a multiple wavelength sensor, reference NP ratio region 740 that delineates physiologically significant NP ratios 630 (FIG. 6), and a comparator 750. In one particular embodiment, the NP ratio region 740 is predetermined from clinically-derived data for one or more parameters of interest, such as SpO₂, HbCO, HbMet, Hbt, fractional oxygen saturation, bilirubin or glucose, to name a few. In another particular embodiment, the NP ratio region 740 is theoretically calculated. The comparator 750 compares the input NP ratios 710 with the NP ratio region 740 and generates a probe-off indicator 770 if any, or more than a predetermine number, of the input NP ratios 710 fall outside of an NP ratio region 740.

[0032] FIG. 8 illustrates a family of parametric NP ratio versus wavelength curves 800 computed from a multiple wavelength sensor, such as referenced above. Each curve represents a different value of a measured parameter, such as SpO₂. For example, there may be a curve for each of SpO₂=70%, 75%, 80%, . . . 100%. Advantageously, such curves more precisely indicate physiologically significant multiple wavelength sensor measurements as compared to a bounded NP ratio region 630 (FIG. 6) such as described with respect to FIGS. 6-7, above.

[0033] FIG. 9 illustrates another embodiment of a physiological parameter confidence measurement system 900 utilizing parametric NP ratio curves, such as described with respect to FIG. 8, above. The confidence measurement system 900 has input NP ratios 910 measured in response to a multiple wavelength sensor, a parameter estimator 920, reference parametric curves 940 and a difference calculator 950. The parameter estimator 920 inputs the NP ratios 910 so as to generate a parameter estimate 930, such as SpO₂, HbCO, HbMet, Hbt, fractional oxygen saturation, bilirubin or glucose, to name a few. The estimated parameter 930 selects one or more of the reference parametric curves 940, which are predetermined from clinically-derived data that is stored in memory or data that is mathematically pre-calculated or calculated in real time and stored in memory. The difference calculator 950 measures the difference between the NP ratios 910 and the selected parametric curve 940. For example, a mean-squared error calculation can be made between the input NP ratios 910 and the selected parametric curve 945. The resulting difference calculation is used as a confidence measure or translated into a confidence measure and a confidence indicator output 960 is generated accordingly. Alternatively, or in addition to a confidence measure, a probe off condition can be indicated if the difference calculation is larger than a predetermined value or the confidence measure is less than a predetermined value. In another embodiment, a correlation calculator is used in place of the difference calculation.

[0034] FIG. 10 illustrates a family of data clusters 1000 shown in two dimensions by way of example. Each data cluster 1000 represents NP ratios clinically measured across a population for specific values 1020 of a selected parameter P, such as P₁, P₂, P₃ and P₄ as shown. Each data cluster 1000 defines a region 1010 of NP ratios measured for a particular parameter value 1020 and has a probability distribution, such as a normal distribution, over the indicated region 1010.

[0035] For example, the clinical data can be organized as a table of known values of P, corresponding NP ratios measured over a population, and the relative number of occurrences of particular NP ratio values for each value of P. The relative number of occurrences of particular NP ratio values for a particular value of P yields an NP ratio probability distribution for that value of P. Thus, each P value 1020 in the table has a corresponding data cluster 1000 of measured NP ratios and an associated probability distribution for those NP ratios.

[0036] FIG. 11 illustrates yet another embodiment of a physiological parameter confidence measurement system 1100 utilizing NP data clusters and corresponding probability distributions, such as described with respect to FIG. 10, above. The confidence measurement system 1100 has input NP ratios 1110 measured in response to a multiple wavelength sensor, a parameter estimator 1120, reference data clusters 1140 and a probability calculator 1150. The parameter estimator 1120 inputs the NP ratios 1110 so as to generate a parameter estimate 1130, such as described with respect to other embodiments, above. In one embodiment, the reference data clusters 1140, such as described with respect to FIG. 10, are stored in a memory device, such as an EPROM. The estimated parameter 1130 is compared with the reference data clusters 1140 so as to determine the closest region 1010 (FIG. 10) or closest overlapping portion of two regions 1010 (FIG. 10). The probability calculator 1150 computes a probability based upon the distribution above the selected region 1010 (FIG. 10). A confidence measure is also derived based upon the calculated probability 1150. In a particular embodiment, the confidence measure is the calculated probability. A confidence indicator 1160 is generated in response to the confidence measure. In one embodiment, if the confidence probability or the calculated confidence measure is below a predetermined threshold, a probe-off indicator 1170 is generated. In particular embodiments, the confidence indicator 1160 or probe-off indicator 1170 or both may be alphanumeric or digital displays, optical indicators or alarms or similar audible indicators, to name a few.

[0037] A physiological parameter confidence measurement system has been disclosed in detail in connection with various embodiments. These embodiments are disclosed by way of examples only and are not to limit the scope of the claims that follow. One of ordinary skill in art will appreciate many variations and modifications.