System and method for measuring venous oxygen saturation using intelligent pulse averaging with integrated EKG and PPG sensors - Patent Application 20070123633

Abstract

To provide a system for non-invasively measuring and determining venous oxygen saturation.SOLUTION: A system using combined electrocardiography (EKG) and photoplethysmography (PPG) sensing, to determine venous oxygen saturation is described. The system uses averaging of similar pulses based on a prior (or n-1) R-to-R pulse wave duration, and a current (or n) R-to-R pulse wave duration for evaluation of the metabolic reserve and / or stress of a patient.SELECTED DRAWING: Figure 10A

Description

[Technical field]

[0001] Related Applications This application is a continuation of U.S. Provisional Patent Application No. 63 / 009,470, filed on April 14, 2020, entitled "PULSE WAVE TRANSIT TIME (PWTT) MEASUREMENT SYSTEM USING INTEGRATED EKG AND PPG SENSORS," U.S. Provisional Patent Application No. 63 / 067,147, filed on August 18, 2020, entitled "SYSTEM FOR IMPROVED MEASUREMENT OF OXYGEN SATURATION, NON-INVASIVE DETECTION OF VENOUS AND ARTERIAL PULSE WAVEFORMS, AS WELL AS DETECTION OF CARBOXYHEMOGLOBIN, HYPERTROPHIC CARDIOMYOPATHY AND OTHER CARDIAC CONDITIONS," and U.S. Provisional Patent Application No. 63 / 067,147, filed on December 28, 2020, entitled "SYSTEMS FOR SYNCHRONIZING DIFFERENT DEVICES TO A CARDIAC This application claims priority to U.S. patent application Ser. No. 17 / 135,936, entitled "SYSTEM AND METHOD FOR GENERATING PULSE WAVEFORMS FROM SYNCHRONIZED ECG AND PPG SYSTEMS," the entire disclosure of which is incorporated herein by reference in its entirety for all purposes.

[0002] The present system relates to a cardiac sensing system that combines electrocardiogram (EKG) and photoplethysmograph (PPG) sensing systems.

[0003] Brief description of the clinical problem: Venous hemoglobin oxygenation in health is often above 80%. While this may seem surprising, this high level of oxygenation represents the metabolic reserve the body can bathe in even if no deep breaths are taken in the last few seconds. In states of stress, that reserve is whittled away and such venous saturation is clinically useful as it provides a measure of the body's oxygen reserve. Currently, this can only be obtained via invasive venous blood gas measurements. Venous oxygen saturation and serum lactate are both used to measure a patient's metabolic reserve and degree of stress, as venous saturation decreases during metabolic stress. Serum lactate rises when tissues are not receiving enough oxygen to meet metabolic demands and tissues turn to anaerobic use of glucose. Currently, the most common measure for assessing metabolic stress is serum lactate, but recent studies such as Serum Lactate Poorly Predicts Central Venous Oxygen Saturation In Critically Ill Patients: A Retrospective Cohort Study by Bisara et.al., PMID: 21516712, DOI: 10.1186 / s40560-019-0401-5 suggest that venous oxygen saturation may be a better early measure of stress before the onset of critical decompensation. Serum measurement of lactate or venous blood gases requires a sterile blood draw capability and a qualified laboratory nearby that can rapidly process venous samples that are placed on ice after blood draw. Thus, the ability to measure venous oxygen saturation noninvasively has great implications for assessing metabolic stress in both resource-rich and resource-limited settings.

[0004] Summary of the invention and cardiac physiology closely related to the invention: In a preferred aspect, the system is a system for determining venous oxygen saturation, the system comprising: (a) a device positionable against the skin of a person; (b) at least one PPG sensor attached to the device for measuring the person's PPG signal at a plurality of wavelengths of light; (c) a plurality of electrodes for measuring the person's EKG signal; and (d) a computer logic system for receiving and analyzing the PPG signal and the EKG signal, the computer logic system comprising: (i) a system for identifying cardiac cycles in the EKG signal; (ii) a system for segmenting the PPG signal into a series of PPG signal segments based on features in the identified cardiac cycles; and (iii) a system for analyzing the PPG signal segments. The system further comprises: (iv) a system for generating a composite signal for each of the plurality of bins; and (v) a system for measuring the person's venous oxygen saturation by: (a) calculating arterial oxygen saturation by comparing the composite signals measured at different wavelengths of light; (b) subsampling the composite signals at two successive signal maxima measured at the different wavelengths of light; and (c) comparing the subsampled composite signals measured at the different wavelengths of light to the calculated arterial oxygen saturation to determine the venous oxygen saturation. Preferably, calculating arterial oxygen saturation by comparing the composite signals measured at the different wavelengths of light includes comparing composite signal prime-over-signal (SPOS) signals, where each composite SPOS signal is a derivative of the composite signal normalized by itself.

[0005] In a preferred embodiment, the system for measuring a person's venous oxygen saturation selects a preferred bin for which the composite signal is used when calculating the person's venous oxygen saturation, the preferred bin corresponding to the bin with the greatest number of PPG signal segments therein and / or the greatest difference between the current R to R value and the previous R to R value. The composite signal may be generated for each bin by summing or averaging the PPG signal segments in the bin. Additionally, the composite signal may be used to generate a composite signal prime-over signal (SPOS), which is the derivative of the composite signal normalized by itself. In such an embodiment, a system may be included for calculating arterial oxygen saturation by comparing composite SPOS signals measured at different wavelengths of light.

[0006] In a preferred embodiment, the system for generating a composite signal for each of the plurality of bins includes a system for removing anomalous PPG signal segments from the calculation of the composite signal by iteratively recalculating the composite signal, for example by comparing the SPOS of each of the PPG signal segments used to calculate the composite signal with the SPOS of the calculated composite signal, removing any outlier PPG signal segments, recalculating the composite signal with the outlier PPG signal segments removed, and repeating the iterations until there are no more outlier PPG signal segments.

[0007] In various preferred physical embodiments illustrated herein, the system is a handheld device, with at least one PPG sensor mounted on the handheld device and with multiple electrode wires extending from or mounted on the handheld device. Alternatively, the system can be disposed in a strap or band disposed around the person's chest or limb, with at least one PPG sensor and multiple electrodes disposed in the strap or band. Alternatively, the system can be disposed in a patch, with at least one of the at least one PPG sensor and multiple electrodes disposed in the patch. The system also provides for data transmission.

[0008] The system provides information about a given patient's metabolic reserve / stress inexpensively and non-invasively. Such knowledge can quickly and safely provide clinicians with important point-of-care information about the clinical course of a patient's recovery or decline without waiting for laboratory results. The bottom line is that analysis of FIG. 1, which shows a top-level flow diagram of a system for estimating venous saturation, shows that arterial hemoglobin saturation is obtained from the main pulse region of the composite PPG signal shape, i.e., the region of greatest slope change (101), and a separate estimate of venous hemoglobin oxygenation is obtained using only the end pulse signal maximum / arterial pulse minimum (102). The two results are then compared to determine how much oxygen reserve is evident.

[0009] The key to this analysis is to understand that the dynamics of the "tissue sandwich" through which the PPG signal is filtered changes slightly at the end of the pulse. This can be seen in FIG. 2. Curve 201 is an experimentally obtained arterial waveform derived from a synthetic IR PPG signal. As can be seen, the waveform is incredibly clean, the result of intelligently similar pulses averaging over many minutes. What is striking is that the decline (or "roll-off") of the arterial pulse from the peak is a simple exponential, or even a steady decline, and the curve does not result in the "hump" seen before the pulse minimum just before the start of the next pulse (202).

[0010] Rather than passive draining, there is active filling of the arterioles in this time frame, essentially priming the "hourglass" structure of arterioles, capillaries (through which blood cells pass one by one), and venules. This priming effect causes a change in the composition of the blood, which is measured by PPG oximetry. Taking advantage of this observation that blood composition is changing just before the end of the pulse, the signal maxima (corresponding to the arterial pulse minima) are collected together and analyzed separately from the PPG signal acquired throughout the pulse. A pictorial representation of this approach is provided in FIG. 3, which shows LED sampling in FIG. 3A, standard PPG arterial hemoglobin oximetry, and end pulse / signal maximum sampling in FIG. 3B. Line 302 shows the arterial pulse sampled via standard oximetry where sampling of the LED signal (301) occurs. Line 303 is the resulting PPG signal. Point 304 shows the signal maximum / pulse minimum. FIG. 3B shows the different sampling performed in end pulse / signal maximum oximetry. Line 306 shows the arterial pulse through which the sampling of the LED signal (305) occurs, but only at the end pulse, where the signal is at a maximum. Line 307 is the resulting PPG signal from such sampling.

[0011] Further depictions of the structures measured in the arterial end pulse can be seen in FIG. 4. In FIG. 4A, the arteriole / capillary / venule structure is shown as an hourglass. Throughout the main part of the arterial pulse, the hourglass representing the arteriole / capillary / venule structure measured in the reflectance oximetry is dominated by maximally oxygenated pre-capillary arteriolar blood (401). However, in the end pulse, there is an accumulation of venular, post-capillary blood, as depicted in 402. FIG. 4B gives another representation of the structure, where the structures measured in the reflectance oximetry are shown as shaded regions (403). Also seen is a graph with a curve of the moving velocity of the blood (404). This shows that the blood cells slow down to a minimum velocity when passing through the capillaries, and there is an asymmetrical velocity before and after the capillaries, explaining the change in blood from arteriole to venule in the end pulse.

[0012] This approach is further illustrated in Figures 5A and 5B, which show the change between sampling points (duration from R to R) as linear and how the PPG signal may change. Area 501 represents endopulse arterial (pre-capillary) blood, area 502 represents endopulse venous (post-capillary) blood, and area 503 is the fixed elements of the structure (mostly connective tissue). In Figure 5A, LED sampling through the structure at endopulse / signal maxima (504) before and after a long pulse results in PPG signals 505 and 506 separated by time 507 (duration from R to R). In Figure 5B, LED sampling through the structure at endopulse / signal maxima (504) before and after a short pulse results in PPG signals 508 and 509 separated by time 510 (duration from R to R).

[0013] The system uses a combination of electrocardiography (EKG) and photoplethysmography (PPG) signals (PPG is also commonly referred to as oximetry, and the two terms are used interchangeably throughout this specification). The former senses the voltage generated by the contraction of the heart muscle, while the latter measures the light absorbed by the tissue. Changes in the PPG signal reflect changes in blood volume, and measurements at different wavelengths allow the determination of oxygen saturation.

[0014] The current system allows for different insights than are currently available using handheld portable PPG systems / devices. The combination of EKG and PPG signals in this system utilizes the pulse wave transit time (or PWTT) and the PPG signal prime over signal (SPOS) curve. PWTT is the time period taken between the heartbeat measured by the onset of the QRS decode and the time blood from the aorta reaches the limb or other body part as determined by the negative spike produced in the SPOS curve, also described as the derivative of the LED signal divided by the signal. U.S. Patent No. 10,213,123, assigned to MocaCare Corporation of Palo Alto, California, describes the use of the signal derivative to determine the change in the LED signal that signals the arrival of the arterial pulse. However, the use of SPOS allows for deeper insights because it normalizes each wavelength signal, allowing the signal prime over signal (SPOS) curves of different wavelengths to be compared.

[0015] Thus, improved arterial oxygen saturation estimates are generated by the system from the SPOS curve using a composite sum / average of similar pulses, with the added ability to generate oxygen saturation for selected segments of the cardiac cycle, specifically endopulse oximetry. The previous (n-1) EKG R to R duration using the R wave peak is calculated, as is the current (n) R to R duration, PWTT, and SPOS. All of these are used by the system to determine the similarity of oximetry pulses, sum / average similar pulses to form a composite pulse, and compare different composite pulses to gain cardiovascular insight.

[0016] Although a decrease in PWTT corresponds to an increase in pulse wave velocity, an increase in velocity does not indicate improved pump function. This is because the aortic valve acts as a "mechanical capacitor" allowing for metered delivery of arterial pulse volume. However, once PWTT is obtained for any given monitoring point on the body, this metric remains relatively stable and changes only gradually in the absence of a sudden change in cardiovascular status (e.g., a sudden change in heart rhythm such as the onset of atrial fibrillation with a rapid ventricular response). Thus, PWTT provides a means to ensure accurate further data collection and analysis. This allows for more reliable extraction of additional information from the signal combination and removal / minimization of introduced noise.

[0017] Measurement of light absorption (according to the Beer-Lambert law) is Measurement(t)=Ke [-Cf(t)] and the signal prime over signal (SPOS) of the measurement is:

number

[0018] The LED signal in photoplethysmography has the following format:

number

[0019]

number

[0020] Furthermore, in healthy individuals, venous flow is assumed to be constant. Current oximetry measurements assume this, and so will this initial study. Given this assumption, the equation reduces to:

number

[0021] Using the properties of the exponential function and its derivatives, we derive the SPOS for the PPG signal at several wavelengths (e.g., IR and red).

number

[0022] Using the fact that the conceptual function Arterial(t) is the same for both the red and IR PPG signals, the SPOS of the signal from the IR LED (SPOS IR ) is the SPOS (SPOS 赤色 ) is directly proportional to (4) SPOS 赤色 =R*SPOS IR or SPOS 赤色 / SPOS IR =R

[0023]

number

[0024] During the ceremony,

number

[0025] In conditions of low levels of carboxyhemoglobin and methemoglobin (except in situations such as carbon monoxide or cyanide poisoning),

number

number

[0026] Ultimately, the formula becomes:

number

[0027] The only unknown is

number

number

number

[0028] This direct proportionality between the SPOS for any wavelength and the sum of the optical absorption coefficients multiplied by the hemoglobin fraction is used extensively by the present system.

[0029] Recordings of EKG, oximetry signals, or their interactions have physiological variability as well as noise. Managing EKG noise has established protocols that have been built up over more than 100 years. Conditioning oximetry signals has a lesser history. Physiological oximetry variability can arise from changes in venous flow (due to voluntary movement, passive movement due to postural changes, or inflation / deflation of a blood pressure cuff / sphygmomanometer), changes in the amount of blood returning to the heart as a result of changes in intrathoracic pressure caused by breathing, or variations in the duration between heartbeats. Noise, or non-physiological variability, can arise from a variety of possibilities, ranging from variations in detector surface pressure and application angle, to ambient light effects on signal collection, to DC drift in the detection circuitry. Whatever the specific source of variation, without an intelligent approach to the signal, it is not possible to distinguish between physiological and non-physiological variations (introduced noise).

[0030] The traditional means of dealing with noise introduced into the oximetry signal is filtering. For example, a commonly used algorithm to detect signal-to-noise ratio utilizes the power within frequencies below 20Hz compared to the power above this frequency (as explained in MaximIntegrated AppNote AN6410.pdf provided by Maxim Integrated Corporation, San Jose, California). This frequency filtering emphasizes the underlying primary rhythm (heart rate) and smooths the appearance of the displayed waveform. However, pulses are not all the same, and treating them as if they were removes valuable information that could be explored for deeper insights.

[0031] An alternative means of minimizing variability is to average the oximetry over many pulses, as described in U.S. Patent No. 10,485,433, assigned to Intel Corporation. This allows for the noise introduced to be minimized, but removes any information that may be gleaned from physiological variability. This approach produces a single homogenized representative pulse at the end of the process. However, pulses are not all the same, and treating them as if they were effectively erases some of the available information.

[0032] Observation of Figure 2 allows a reevaluation of the assumption of a flat venous blood profile (as in equation (1)). The combination of more or less arteriolar filling / emptying and more or less right ventricular filling with a different R to R is seen in the variation of the PPG signal maximum, with both venular and arteriolar volumes varying at the signal maximum (Figure 5). The composition ratio of arterioles and venules in this delta volume is not yet completely clear. However, this approach shows that the enlargement of the difference between the endo-pulse oximetry and the arterial hemoglobin oxygen saturation calculated from the ascending pulse (main pulse oximetry) reflects the descending venous hemoglobin oxygen saturation. [Brief description of the drawings]

[0033] [Figure 1] 1 is a top-level flow chart of the operation of the present system. [Diagram 2] Figure 2 shows the PPG signal filtered through a "tissue sandwich", showing only a slight change at the end of the pulse. [Figure 3A] FIG. 13 is a diagram of LED sampling for standard PPG arterial hemoglobin oximetry. [Figure 3B] FIG. 11 is a diagram of end pulse / signal maximum sampling. [Figure 4A] Illustrate the anatomy of an arteriole / capillary / venule shown as an hourglass. [Figure 4B] The structure corresponds to FIG. 4A, measured by reflectance oximetry. [Figure 5A] 4A and 4B at the end pulse / signal maximum before and after a long pulse. [Figure 5B] 4A and 4B at the end pulse / signal maximum before and after a short pulse. [Figure 6] 1 shows the process of R-wave peak refinement used to generate t0n. [Figure 7] The nomenclature and data structure used in the description of this system are shown below. [Figure 8] The instructions and data structures used in the description of the system are also shown. [Figure 9] FIG. 2 is an exemplary diagram of various physical components of the system. [Figure 10A] 1 shows various views of a handheld embodiment of the system with PPG and EKG sensors attached to or mounted on the handheld embodiment. [Figure 10B] 1 shows various views of a handheld embodiment of the system with PPG and EKG sensors attached to or mounted on the handheld embodiment. [Figure 10C] 1 shows various views of a handheld embodiment of the system with PPG and EKG sensors attached to or mounted on the handheld embodiment. [Figure 10D] 1 shows various views of a handheld embodiment of the system with PPG and EKG sensors attached to or mounted on the handheld embodiment. [Figure 11] FIG. 10B is a cutaway view of a portion of the device of FIGS. 10A-10D showing an optical waveguide adjacent to a PPG sensor. [Figure 12A] FIG. 12 is a diagram of the system of FIGS. 10A-11 collecting a PPG signal from a human finger. [Figure 12B] FIG. 12 is a diagram of the system of FIGS. 10A-11 collecting PPG signals from the outside of a person's arm. [Figure 13] FIG. 1 shows EKG and PPG signals measured over time and their corresponding generated SPOS signals. [Figure 14] FIG. 11 is a diagram of one-sided Gaussian fitting. [Figure 15] 1 illustrates the time relationship of the end pulse / pre-pulse area of ​​interest versus the SPOS negative spike fitting window. [Figure 16] 1 illustrates a time correlation comparison of EKG and PPG signals showing their relationship in creating a two heartbeat dependency relationship showing the current and previous "R to R" [Figure 17A] Illustrates the number of pulses in each bin for a run of a patient with normal sinus rhythm. [Figure 17B] Illustrates filling of current R to R and previous R to R matrices for the purpose of determining endopulse oximetry (maximum difference in R to R duration) and selection or "fallback" of second tier bins using intermediate bins that provide maximum PPG signal difference allowing estimation of venous oxygen saturation. [Figure 18] FIG. 1 is a top-level block diagram of the endpulse / venous oxygen saturation calculation. [Figure 19] 4 is an exemplary algorithm for preparing a pulse data set in accordance with the present system. [Figure 20] Illustrates the derivation of pulse wave transit time (PWTT). [Figure 21] 4 shows the end pulse / venous oxygen saturation calculation. [Figure 22] 1 illustrates an exemplary embodiment of the system disposed on a chest strap. [Figure 23] FIG. 23 is a cross-sectional view of the patient corresponding to FIG. 22. [Figure 24] 1 illustrates an exemplary embodiment of the system incorporating a biceps strap and electrodes extending therefrom. [Diagram 25] FIG. 25 is a cross-sectional view of the patient corresponding to FIG. 24. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0034] The central element of the system is the identification and manipulation of PPG signals based on the duration of the previous R to R and the current R to R. The system then generates a composite pulse from similar pulses.

[0035] According to preferred aspects disclosed in U.S. Provisional Patent Application No. 62 / 955,196, entitled "A System For Synchronizing Different Devices To A Cardiac Cycle," filed December 30, 2019, and U.S. Patent Application No. 17 / 135,936, entitled "SYSTEMS FOR SYNCHRONIZING DIFFERENT DEVICES TO A CARDIAC CYCLE AND FOR GENERATING PULSE WAVEFORMS FROM SYNCHRONIZED ECG AND PPG SYSTEMS," filed December 28, 2020, which are incorporated by reference in their entireties herein, the system uses a specific trigger to set time=0 for each beat (e.g., EKG R-wave peak), and then stores each pulse from this starting point until a full cycle of sensor data is completed, for example with an LED oximetry signal that goes from maximum to minimum and back to maximum, which results in a waveform that is longer than a single pulse length. The next pulse waveform has t=0 at the next EKG R wave peak, so recording of the next heartbeat begins before recording of the last pulse waveform is completed. In absolute terms, the time corresponding to t=0 of the nth pulse is referred to as time t0n throughout the remainder of this specification.

[0036] Figure 6 shows the process of R-wave peak refinement used to generate t0n. This example shows how the algorithm determined the polarity of this collection to be negative (wires reversed) and therefore the R-waves are negative. The t0n of the R-wave peak is found using polynomial fitting (602) and interpolation to the EKG data points (601) and is used to define the pulse data set.

[0037] 7 and 8 show the nomenclature and data structure used in the description (unless otherwise specified, PWTT=PWTTIR and PPG signal=PPG signalIR). The t0n time instant is then used to define a pulse data set having PPG signals at multiple wavelengths (here red, infrared, and green). Stored with the PPG signals are the previous R to R values, the current R to R values, the derived signal for each wavelength prime-over signal (SPOS), and the pulse wave transit time (PWTT) for each wavelength. Note the maximum value of the first PPG signal (701) and the maximum value of the second PPG signal (702). FIG. 8 shows the structure of a composite pulse data set constructed from a group of pulse data sets based on a defined criteria (e.g., similar previous R to R duration or current R to R duration). Note that the duration of the PPG waveform is longer than a single cardiac cycle and is long enough to reliably capture both the first PPG signal maximum (801) and the second PPG signal maximum (802).

[0038] 9-11 show a preferred device implementation of the system. The block diagram of the device shows the elements of the device / system, including multiple wavelength LEDs (901) and photodiode detectors (902) and EKG input from electrodes (903) applied to the left and right chest (or left and right upper limbs). In a preferred embodiment, the signals are then fed to a processing unit (904) running "on-chip" logic that generates a composite pulse data set from the raw signals. The composite pulse data set is then communicated to an "off-device" display / computing unit (905) via either wireless or direct cable connection, which provides the user with estimates of final end pulse oxygenation, arterial main pulse oxygenation, and the resulting venous oxygenation with many graphical options (such as change over time). There is also on-device storage (906) for code, as well as buffering and packetized transfer of data. In an alternative embodiment, the processing unit simply coordinates communication of the raw ECG and PPG signal data to an external computing / display device that processes all aspects of the hydration level estimation logic. In yet another embodiment, all aspects of hydration level estimation are performed by the processing unit, including rendering graphics and reporting oxygen saturation, in which case the external computing / display device provides only display functionality.

[0039] 10A-D show various views of the PPG acquisition device: 1001 shows the light guide (in front of the LEDs and detectors), 1002 shows the optionally integrated EKG electrodes, and 1003 shows the plug-in connector sites for the EKG lead wires to the adhesive EKG electrodes (for the right and left chest).

[0040] Figure 11 shows details of the PPG head with an optical waveguide (1101) that abuts the LEDs and detectors (1102) inside the device. The optical waveguide allows collection of the PPG signal at sites other than the finger.

[0041] Figures 12A and 12B show the device in use collecting PPG signals from the fingers (Figure 12A) and the outer upper arm (Figure 12B). The PPG measuring end of the device is applied to the skin in a stable manner and allowed to sit for 1-2 minutes or more to obtain a PPG measurement. EKG electrodes are applied to the left and right sides of the torso (or upper extremities) and connected to plugs on the smaller end of the device.

[0042] Returning to Figure 1, PPG and EKG signals are acquired. PPG waveform selection is performed to eliminate abnormal beats that are significantly different from the majority of pulses, such as premature ventricular beats, which are examples of cardiac contractions that do not change properly from the previous beat.

[0043] 13 shows an EKG signal (1301), an EKG R-wave peak (1302), a PPG signal segment (contained within the pulse data set) (1303, 1304, 1305), an SPOS signal (contained within the pulse data set) (1306, 1307, 1308), and a PWTT using SPOS (also contained within the pulse data set) (1309, 1310, 1311). This presents one selection of a PPG signal that has a full time duration of both the PPG signal and an SPOS that is longer than the R to R duration (in the current implementation, red, infrared, and green are used, but the approach is not limited to using only these).

[0044] The present method and system of intelligent pulse averaging counters the effects of drift in "K" (as seen in Equation 1) associated with absorption from stationary elements in the tissue being analyzed. With averaging, some pulses will have an upward drift in K and some will have a downward drift, leaving the averaged pulse with more options for comparing data points across the composite pulse width.

[0045] It shows how the SPOS generates similarly shaped curves for LED signals of different wavelengths, and for a particular wavelength

number

[0046] Given that the shapes of the SPOS curves are similar, such a fit can be applied to one wavelength to obtain a fitted curve. Fitting to another wavelength only requires finding the magnitude required to best fit that curve. For example, if f(t) best fits the infrared LED SPOS, then the "A" required to best fit A*f(t) to the SPOS of the red LED signal will give the arterial oxygen saturation, similar to Equation 1. The difference with the standard formulation is that this fit is based on more time points (up to 50 for slower heart rates) than the two (maximum and minimum) used in the standard formulation.

[0047] Figure 14 illustrates this concept using one-sided Gaussian derivative fitting. Curve 1401 is the data points of a collected composite IR SPOS signal where a fitting window 1402 selects the negative SPOS "spikes". Curve 1403 shows the data points of the window in an expanded plot, along with the one-sided Gaussian derivative fit curve 1404.

[0048] The selected fitting window interval (SPOS “negative spike”), or a subset thereof (e.g., the right half of the rising SPOS of the “negative spike”), represents a unique period of a single dominant and coherent physiological event, specifically, the contraction of the left ventricle while the aortic valve is opening, clearly separated from other confounding physiological features. This allows for the extraction of parameters that can be applied to the entire pulse waveform of the PPG sensor.

[0049] As described in the Physiology Summary above, the interval immediately preceding this fitting window of the SPOS "negative spike" represents yet another unique interval. Figure 15 shows the time relationship of the end pulse / prepulse area of ​​interest to the fitting window of the SPOS negative spike. Time 1501 identifies the end pulse point, and region 1502 indicates the excess SPOS PPG beyond the expected linear or exponential "roll-off" (the shaded region in Figure 15 corresponds to the shaded region in Figure 2). Region 1503 identifies the window of the negative SPOS "spike" used to estimate arterial hemoglobin oxygen saturation.

[0050] Creating a two-beat complex for venous saturation analysis: A two-beat composite selection for a longer pulse train in atrial fibrillation (resulting in random R to R duration) is shown in Figure 16. The signal from a two-electrode single-lead EKG (curve 1601) is plotted aligned in time with an infrared (IR) LED PPG signal (curve 1602). Infrared wavelengths are shown and used to select pulses for further analysis because they have relatively equal absorption from venous and arterial blood.

[0051] Due to the accumulation of similar two-beat complexes (based on similar n-1 R to R and n R to R durations), the composite pulse configuration can range from one pulse minimum / signal maximum to the next pulse minimum / signal maximum. This formulation allows for comparison of pulse minimum / signal maximum at the start of a pulse and at the end of a pulse, making additional information available about the cardiovascular status of an individual. FIG. 16 shows an upper / lower alignment of an EKG signal (1601) and a PPG signal (1602) illustrating the steps of generating a composite PPG wave for the purpose of deriving venous oxygen saturation.

[0052] The EKG (1601) signal above shows a series of pulses labeled A through I. Each of these pulses vary in duration, some shorter than others. A two-beat dependency relates two successive beats together, with the important features being the R to R duration of the first beat and the PPG signal of the second beat. This is the dependency depicted in brackets (1603) that seeks to bring together the R to R duration of beat "B" (1604) and the PPG signal of beat "C" (1605). More important in this analysis is the current R to R duration, which in this composite is the R to R duration of pulse "C" (1606). Of note in the bracketed composite 1603 is the pairing of a long n-1 R to R followed by a short n R to R.

[0053] Pulses B and C are analyzed together, and the R to R duration of B and the R to R duration of C put this two-beat complex into a long n-1 R to R / short n R to R "bin." Next, pulses C and D are considered together, and the R to R duration of C and the R to R duration of D put this two-beat complex into a short n-1 R to R / long n R to R "bin." Next, pulses D and E are considered together, and the R to R duration of D and the R to R duration of E put this two-beat complex into a long n-1 R to R / medium n R to R "bin." Next, pulses E and F are considered together, and the R to R duration of E and the R to R duration of F put this two-beat complex into a medium n-1 R to R / short n R to R "bin." Next, pulses F and G are considered together and the R to R duration of F and the R to R duration of G put this two beat complex into a short n-1 R to R / long n R to R "bin." Next, pulses G and H are considered together and the R to R duration of G and the R to R duration of H put this two beat complex into a long n-1 R to R / medium n R to R "bin."

[0054] As this analysis reveals, atrial fibrillation offers a wide range of permutations of n-1 R to R and n R to R durations. This allows analysis using short-long and long-short n-1 and n R to R durations, combinations that reveal the greatest change in PPG signal maximum. However, in normal sinus rhythm, it is more difficult to select combinations that help reveal differences in signal maximum. FIG. 17A shows the number of pulses in each bin for runs of a patient with normal sinus rhythm. Note that the diagonal lines corresponding to the same n-1 and n R to R durations are most densely populated, and the short-long and long-short bins are the least densely populated. FIG. 17B shows the preferred filling of the current R to R vs. previous R to R matrix (1701) for the purpose of determining endopulse oximetry (the greatest difference in R to R duration), and the selection or "fallback" of the second tier of bins (1702) using the intermediate bin that provides the greatest difference in PPG signal that allows for an estimation of venous oxygen saturation.

[0055] Measurement of venous oxygen saturation These two-beat complexes define both the beginning and ending composite PPG signal, and therefore both the first and second signal maxima (pulse minima) are defined. The method described here also allows for the estimation of venous saturation, since the accumulation of similar two-beat complexes mitigates the effects of DC drift. A top-level block diagram of the end pulse / venous oxygen saturation calculation can be seen in Figure 18.

[0056] R-wave peak refinement for pulse "n" is performed by curve fitting and interpolation (1801) before determining the previous (n-1) R to R duration and the current (n) R to R duration, and then the (n-1) and current (n) R to R durations for pulse dataset "n" are incorporated into pulse dataset "n" (1802). PPG signals are collected and a process of outlier rejection is performed (including, but not limited to, using accelerometer input to determine data is corrupted and cross-checking with multiple LED PPG sensors, 1803). Once the PPG signal for the current pulse dataset is selected, the pulse dataset is considered along with all available previous pulse datasets and their PPG signals, each associated with the previous (n-1) R to R duration and the current (n) R to R duration.

[0057] The available pulse data sets are then sorted into a 3x3 bin matrix of previous R to R and current R to R, each considered and deemed to be of short, medium, or long duration (1804). Dynamic boundary adjustment is used to ensure as equal a number as possible between bins, with normal sinus rhythm resulting in few available pulse data sets in bins off the short-short, medium-medium, and long-long diagonals (see FIG. 17). After all pulse data sets have been assigned to bins, an optimal bin is selected (1805), e.g., the bin containing the greatest number of pulse data sets and revealing the greatest change in signal maximum. Once the optimal bin is established, an initial composite pulse data set is formed by adding together the corresponding PPG signals of each wavelength for each pulse data set in the bin (1808).

[0058] With the binned pulse data sets and the initial composite pulse data set in hand, a pruning loop is performed for each bin (1806) to filter out pulse data sets containing noisy or otherwise anomalous PPG signals that passed the coarser outlier rejection. For each pulse data set in the bin, and for each wavelength in the pulse data set, the PWTT of the wavelength is compared against the PWTT for that wavelength in the composite pulse data set (collection of all pulses). If the PWTT of two of the current three wavelengths (red, green, IR) is within 15% of the PWTT of the composite pulse data set, the pulse data set is left in the composite. If not, the pulse data set is rejected ("pruned") and the process is run again with the remaining pulse data set. The pruned pulse data set is removed from the bin and subtracted from the composite pulse data set. If the number of pulse data sets falls below a specified threshold for the number in a bin (good results have been obtained with numbers up to four), additional pulse data sets are added before reporting the results. This algorithm can be seen in FIG. 19. Figure 20 shows the derivation of the pulse wave transit time (PWTT) using the signal prime over signal (SPOS(t)) curve for each wavelength of the PPG signal (t) together with interpolation and (negative) peak refinement.

[0059] The end pulse / venous oxygen saturation calculation then proceeds as shown in Figure 21. The venous oxygen saturation calculation follows the derivation shown in Appendix B using a two-point composite signal composed of PPG signal maxima corresponding to non-coincident R to R durations from two beat complexes (1807). The results are averaged and the venous partial oxygen saturation is reported (1809).

[0060] System Operation: In the various exemplary embodiments of the system below, alternative operational options are presented, and it should be understood that the system may be embodied in any of the systems described herein, and is not limited to only the various exemplary embodiments described below.

[0061] FIG. 22 illustrates the use of a cross-chest chest strap (2201) with integrated electrodes (2202) contacting the left and right chest and equipped with a detector-equipped LED device (2203).

[0062] FIG. 23 illustrates a cross section of a chest strap (2301) across the chest with integrated electrodes (2302) contacting the left and right chest and an LED device with detector (2303).

[0063] Figure 24 illustrates the use of a bicep strap (2401) with an integrated electrode (2402) and an LED device with LED detector (2403). A second electrode piggybacks onto existing telemetry wiring (2404).

[0064] Figure 25 illustrates a cross section of a biceps strap (2501) with an integrated electrode (2502) and a detector LED device (2503). A second electrode piggybacks onto existing telemetry wiring (2504).

[0065] The advantage of a chest or arm strap or band is that the band / strap provides a normal force to the PPG sensor's LEDs to get a good signal from the chest wall. In embodiments where chest or arm straps are used, optional "traction" may be provided on the inside of the strap, similar to the silicone / adhesive beads found on the inside of standard bike shorts to keep them from riding up the legs.

[0066] Appendix A: Background of Cardiovascular Physiology In normal health, the delivery of oxygen and glucose to tissues is sufficient for tissues to meet their energy needs, either through aerobic glycolysis, the process of breaking down glucose using oxygen and releasing much more energy than anaerobic glycolysis, or through fermentation (the metabolism of glucose without oxygen). Aerobic glycolysis breaks down glucose into water and carbon dioxide, whereas anaerobic glycolysis breaks down glucose into lactic acid. In health, lactic acid is low and the pH (affected by the presence of lactic acid) is maintained at about 7.4. Anaerobic metabolism with the production of lactic acid allows muscles to temporarily access extra energy when tissue demand is high and there is insufficient delivery of oxygen to "burn" the available glucose (such a situation is seen when sprinting a short distance). The lactic acid thus produced is removed from the bloodstream by the liver and converted back to glucose once the physiological stress is resolved. This allows for the complete aerobic glycolysis of the previously fermented glucose.

[0067] However, when that physiological stress is sustained rather than episodic, many things start to go wrong. This can occur when the metabolism is greatly increased by an infection, or when the delivery capacity is suddenly reduced, as in a heart attack, or in the setting of a baseline reduction in cardiac pumping function, or even in the setting of otherwise moderate stress. In either case, the oxygen demand of tissues increases relative to what the cardiovascular system can deliver. The oxygen acquired in the lungs cannot fully replace the oxygen removed from the capillary bloodstream. In this situation, arterial hemoglobin oxygenation decreases, and venous blood oxygen saturation decreases further as the body uses up the oxygen reserve in the venous blood that was accumulated before the stress (venous blood oxygen saturation can exceed 80% in normal health without stress). All of this results in a large difference between arterial and venous oxygen.

[0068] Further declines in venous oxygenation will eventually cause lactate levels to begin to rise, but recent studies have shown that a measurable decline in venous oxygenation precedes the rise in lactate, and that this decline provides clinically useful information. However, to measure venous oxygen, a venous blood gas sample is required. This is obtained by drawing blood that is immediately placed on ice and sent to a qualified laboratory. All of this is a relatively expensive and invasive procedure, with the minimum required time being approximately 10-15 minutes when performed STAT.

[0069] Circulatory shock causes insufficient oxygen delivery, resulting in mitochondrial hypoxia. When mitochondrial oxidative phosphorylation fails, energy metabolism becomes dependent on anaerobic glycolysis. Anaerobic glycolysis rapidly increases cellular lactate production, which in turn increases blood levels. In severe infections, blood lactate concentrations fluctuate in proportion to the continuing deficit in tissue oxygenation. The patient's ability to clear blood lactate indicates restoration of oxygen delivery with resuscitation. Studies have shown that lactate clearance of 10% or greater predicts survival from septic shock.

[0070] Studies have shown that a decrease in venous oxygen can provide useful information earlier than a rise in lactate. Unfortunately, this requires continuous invasive monitoring via a central venous line (or Swan-Ganz intracardiac catheter) and / or repeated blood draws.

[0071] Appendix B: Calculation of Venous Hemoglobin Saturation At the minimum value of the pulse (maximum value of the LED signal),

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[0072] Collect the maxima of the LED signals (A and B separated in time by the duration from R to R), treat them as time-varying signals, and rearrange the equation as follows:

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[0073] There is some change in blood composition towards the end of the pulse as the arteriolar blood primes the "hourglass" structure of arterioles, capillaries, and venules towards the end of the pulse. This suggests that a reevaluation of the assumption of a flat venous blood profile can be done by using the following to model the change in blood composition at the PPG signal maximum: (4) Delta arterial blood = gamma * delta volume (5) Delta venous blood = (1-gamma)*delta volume

[0074] Gamma can be anywhere between 1 and 0.5.

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[0075] Again, assuming there is only deoxyhemoglobin (Hb) and oxyhemoglobin (HbO2),

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[0076] If the only unknowns are HbO2 and dVolume(t) / dt, then it can be solved for HbO2 when done at two or more different wavelengths (e.g., red, infrared, but not limited to). At each wavelength,

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Claims

1. 1. A computer logic system for receiving and analyzing PPG and EKG signals, comprising: (i) a system for obtaining (a) a PPG signal from a device including at least one PPG sensor for measuring the person's PPG signal at multiple wavelengths of light, and (b) an EKG signal from a multiple electrodes coupled to the person; (ii) a system for identifying cardiac cycles in an EKG signal; (iii) a system for segmenting the PPG signal into a series of PPG signal segments based on the identified cardiac cycle features; (iv) a system for sorting PPG signal segments into a plurality of bins, each bin based on a duration of a previous R to R cardiac cycle and a current R to R cardiac cycle; (v) a system for generating a composite signal for each of a plurality of bins; (vi) determining the venous oxygen saturation of the person; (a) calculating arterial oxygen saturation based on a composite signal measured at different wavelengths of light, comprising comparing composite prime-over-signal (SPOS) signals, each composite SPOS signal being a derivative of the composite signal normalized by itself; (b) subsampling the composite signal at two consecutive signal maxima measured at different wavelengths of light; (c) determining venous oxygen saturation based on the subsampled composite signals measured at the different wavelengths of light and the calculated arterial oxygen saturation. A system comprising:

2. 10. The system of claim 1, wherein the system for measuring venous oxygen saturation of a person selects a preferred bin in which the composite signal is used when calculating the person's venous oxygen saturation.

3. The system of claim 2 , wherein the preferred bin corresponds to the bin having the greatest number of PPG signal segments therein.

4. 4. The system of claim 3, wherein the preferred bin corresponds to the bin having the largest difference between the current R to R value and the previous R to R value.

5. The system of claim 1 , wherein the system for generating a composite signal for each bin comprises a system for summing or averaging PPG signal segments for the bins.

6. 6. The system of claim 5, wherein the composite signal is used to generate a composite prime-over signal (SPOS) that is the derivative of the composite signal normalized by itself.

7. The computer logic system 7. The system of claim 6, further comprising: (vii) a system for calculating arterial oxygen saturation based on the composite SPOS signals measured at different wavelengths of light.

8. The system of claim 1 , wherein the system for generating a composite signal for each of a plurality of bins comprises a system for removing anomalous PPG signal segments from a calculation of the composite signal.

9. A system for removing anomalous PPG signal segments from a computation of a composite signal, comprising: comparing the SPOS of each of the PPG signal segments used to calculate the composite signal to the SPOS of the calculated composite signal; Remove outlier PPG signal segments; recalculating the composite signal with the outlier PPG signal segments removed; The system of claim 8 , comprising a system for iteratively recalculating the composite signal by repeating the iterations until there are no outlier PPG signal segments.

10. The system of claim 1 , wherein the device is a handheld device having at least one PPG sensor mounted thereon and a plurality of electrode wires extending therefrom.

11. The system of claim 10 , wherein the device is a handheld device having at least one PPG sensor mounted thereon and at least one of a plurality of electrodes mounted thereon.

12. The system of claim 10 , wherein the optical waveguide is interposed between at least one PPG sensor on the device and the person's skin.

13. 2. The system of claim 1, wherein the device is disposed within a strap or band disposed around the person's chest or limb such that the at least one PPG sensor and the plurality of electrodes are disposed within the strap or band disposed around the person's chest or limb.

14. The system of claim 1 , wherein the device is a patch having at least one PPG sensor and at least one of the plurality of electrodes disposed therein.

15. A computer logic system is disposed within the device such that a composite signal is generated within the device, and a system for measuring venous oxygen saturation comprises: the composite signal to a remote computer system for analysis; or Measured PPG and EKG signals to a remote computer system for analysis A data transmission system for transmitting one or both of The system of claim 1 , comprising:

16. 1. A method for receiving and analyzing PPG and EKG signals, comprising: obtaining, via a computer logic system, (a) a PPG signal from a device including at least one PPG sensor mounted thereon for measuring the person's PPG signal at multiple wavelengths of light, and (b) an EKG signal from multiple electrodes coupled to the person; identifying, via a computer logic system, cardiac cycles in the EKG signal; Segmenting, via a computer logic system, the PPG signal into a series of PPG signal segments based on the identified cardiac cycle features; Sorting, via a computer logic system, the PPG signal segments into a plurality of bins, each bin based on a duration of a previous R to R cardiac cycle and a current R to R cardiac cycle; generating, via a computer logic system, a composite signal for each of a plurality of bins; A person's venous oxygen saturation is measured through a computer logic system. (a) calculating, via a computer logic system, an arterial oxygen saturation based on a composite signal measured at different wavelengths of light, comprising comparing composite prime-over-signal (SPOS) signals, each composite SPOS signal being a derivative of the composite signal normalized by itself; (b) subsampling, via a computer logic system, the composite signal at two successive signal maxima measured at different wavelengths of light; (c) determining, via a computer logic system, a venous oxygen saturation based on the subsampled composite signals measured at the different wavelengths of light and the calculated arterial oxygen saturation; By carrying out A method comprising:

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