Method and System to Assess Disease Using Dynamical Analysis of Cardiac and Photoplethysmographic Signals

Abstract

The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical and/or geometric properties of Poincar map of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.

Claims

1. A method for non-invasively assessing a disease state or abnormal condition of a subject, the method comprising: obtaining, by one or more processors, a first biophysical signal data associated with a first photoplethysmographic signal and a second photoplethysmographic signal, wherein the first biophysical data set has been acquired over multiple cardiac cycles of the subject; obtaining, by the one or more processors, a second biophysical signal data set associated with a cardiac signal, wherein the second biophysical data set has been acquired over the multiple cardiac cycles; determining, by the one or more processors utilizing at least a portion of the first and second biophysical signal data sets, one or more values associated with one or more synchronicity features, including a first value associated with a first synchronicity feature and second value associated with a second synchronicity feature, wherein the first and second synchronicity features each characterizes one or more synchronicity dynamical properties between the first and second biophysical signal data sets; and determining, by the one or more processors, an estimated value for presence of the disease state or abnormal condition based on the one or more values associated with the synchronicity features, wherein the estimated value for the presence of the disease state or abnormal condition is outputted for use in a diagnosis of the disease state or abnormal condition or to direct treatment of the disease state or abnormal condition.

2. The method of claim 1, wherein the estimated value for the presence of the disease state or abnormal condition comprises an assessed indication or estimate of at least one of presence, non-presence, and severity of elevated or abnormal left ventricular end-diastolic pressure (LVEDP).

3. The method of claim 1, wherein the disease state or abnormal condition is selected from the group consisting of coronary artery disease, pulmonary hypertension, pulmonary arterial hypertension, pulmonary hypertension due to left heart disease, rare disorders that lead to pulmonary hypertension, left ventricular heart failure or left-sided heart failure, right ventricular heart failure or right-sided heart failure, systolic heart failure, diastolic heart failure, ischemic heart disease, and arrhythmia.

4. The method of claim 1, wherein the first value associated with the first synchronicity feature is determined from an analysis selected from the group consisting of: a statistical analysis or a dynamical analysis of values of the cardiac signal at a landmark defined by both the first photoplethysmographic signal and the second photoplethysmographic signal over the multiple cardiac cycles; a statistical analysis or a dynamical analysis of values of one of the first or second photoplethysmographic signals at a landmark defined in the cardiac signal over the multiple cardiac cycles; a statistical analysis or a dynamical analysis of time interval values between a) a first set of landmarks defined between the first and second photoplethysmographic signals and b) a second set of landmarks defined in the cardiac signal over the multiple cardiac cycles; and a statistical analysis or a dynamical analysis of phase relation values between i) periods of one of the first or second photoplethysmographic signals and ii) periods of the cardiac signal over the multiple cardiac cycles.

5. The method of claim 4, wherein the second value associated with the second synchronicity feature is determined from the statistical analysis or the dynamical analysis of the values of the cardiac signal at the landmark defined by both the first and second photoplethysmographic signals over the multiple cardiac cycles, wherein the landmark defined by both the first and second photoplethysmographic signals is defined at a time when the values of the first and second photoplethysmographic signals intersect.

6. The method of claim 4, wherein second value associated with the second synchronicity feature is determined from the statistical analysis or the dynamical analysis of the values of one of the first and second photoplethysmographic signals at the landmark defined in the cardiac signal.

7. The method of claim 4, wherein the landmark defined in the cardiac signal includes a peak associated with ventricular depolarization.

8. The method of claim 4, wherein the landmark defined in the cardiac signal includes a peak associated with ventricular repolarization or atrial depolarization.

9. The method of claim 4, wherein the second value associated with the second synchronicity feature is determined from the statistical analysis or the dynamical analysis of the time interval values between i) the first set of landmarks defined between the first photoplethysmographic signal and the second photoplethysmographic signal and ii) the second set of landmarks defined in the cardiac signal.

10. The method of claim 1, wherein the first and second biophysical signal data sets are obtained and analyzed to investigate complex, non-linear dynamical properties of the heart.

11. The method of claim 9, wherein the second set of landmarks defined in the cardiac signal includes peaks in the cardiac signal associated with ventricular repolarization or atrial depolarization.

12. The method of claim 9, wherein the first set of landmarks defined by both the first and second photoplethysmographic signals are defined at times when the values of the first and second photoplethysmographic signals signal intersect.

13. The method of claim 4, wherein the second value associated with the second synchronicity feature is determined from the statistical analysis or the dynamical analysis of the phase values between the periods of one of the first or second photoplethysmographic signals and the periods of the cardiac signal.

14. The method of claim 1 further comprising: causing, by the one or more processors, generation of a visualization of the estimated value for the presence of the disease state or abnormal condition, wherein the generated visualization is rendered and displayed at a display of a computing device and/or presented in a report.

15. The method of claim 1, wherein the first value associated with the first synchronicity feature is determined by: determining, by the one or more processors, a histogram having one or more distribution of the synchronicity properties of the first and second biophysical signal data sets; and determining a value of a first statistical parameter of the histogram, wherein the first statistical parameter of the histogram is selected from the group consisting of mean, mode, median, skew, kurtosis, and standard deviation of one or more distributions defined in the histogram, and wherein the first statistical parameter is used in the determining of the estimated value for the presence of the disease state or abnormal condition.

16. (canceled)

17. The method of claim 4, wherein the first value associated with the first synchronicity feature are determined by: determining, by the one or more processors, a Poincar map of the values of the cardiac signal, the values of the one or second photoplethysmographic signal, the time interval values, or the phase relation values determined from the analysis; and determining a value of a geometric parameter of a shape fitted to a cluster defined in the Poincar map, wherein the value of the geometric parameter is used in the determining of the estimated value for the presence of the disease state or abnormal condition.

18. The method of claim 17, wherein the Poincar map is generated by iteratively plotting i) an x-axis, at a first index x1 and a second index x, values of the cardiac signal, the values of one of the first or second photoplethysmographic signal, the time interval values, or the phase relation values and ii) in a y-axis, at the second index x and a third index x+1, values of the cardiac signal, the values of one of the first or second photoplethysmographic signal, the time interval values, or the phase relation values determined from the analysis.

19. The method of claim 17, wherein the time interval value is defined between the second set of landmarks of the cardiac signal and the first set of landmarks defined at a crossover between the first and second photoplethysmographic signals.

20. The method of claim 17, wherein the values of the cardiac signal are amplitude signal values of the cardiac signal at a crossover landmark defined between the first and second photoplethysmographic signals.

21. The method of claim 17 wherein the values of one of the first or second photoplethysmographic signals are amplitude signal values of a at a respective landmark defined in the cardiac signal.

22. A system comprising: one or more processors; and a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors cause the one or more processors to: obtain a first biophysical signal data set associated with a first photoplethysmographic signal and a second photoplethysmographic signal, wherein the first biophysical data set has been acquired over multiple cardiac cycles of the subject; obtain a second biophysical signal data set associated with a cardiac signal, wherein the second biophysical data set has been acquired over the multiple cardiac cycles; determine, utilizing at least a portion of the first and second biophysical signal data sets, one or more values associated with one or more synchronicity features, including a first value associated with a first synchronicity feature and a second value associated with a second synchronicity feature, wherein the first and second synchronicity features each characterizes one or more synchronicity dynamical properties between the first and second biophysical signal data sets; and determine an estimated value for presence of the disease state or abnormal condition based on the one or more values associated with the synchronicity features, wherein the estimated value for the presence of the disease state or abnormal condition is outputted for use in a diagnosis or to direct treatment of the disease state or abnormal condition.

23-43. (canceled)

44. A non-transitory computer readable medium having instructions stored thereon, wherein execution of the instructions by one or more processors cause the one or more processors to: obtain a first biophysical signal data set associated with a first photoplethysmographic signal and a second photoplethysmographic signal, wherein the first biophysical data set has been acquired over multiple cardiac cycles of the subject; obtain a second biophysical signal data set associated with a cardiac signal, wherein the second biophysical data set has been acquired over the multiple cardiac cycles; determine, utilizing the first and second biophysical signal data sets, one or more values associated with one or more synchronicity features, including a first value associated with a first synchronicity feature and a second value associated with a second synchronicity feature, wherein the first and second synchronicity features each characterizes one or more synchronicity dynamical properties between the first and second biophysical signal data sets data set; and determine an estimated value for presence of the disease state or abnormal condition based on the one or more values associated with the synchronicity features, wherein the determined estimated value for the presence of the disease state or abnormal condition is outputted for use in a diagnosis or to direct treatment of the disease state or abnormal condition.

45. The method of claim 1, further comprising: determining, by the one or more processors, the one or more synchronicity features as candidate features in a machine learning model.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and together with the description, serve to explain the principles of the methods and systems.

[0083] Embodiments of the present invention may be better understood from the following detailed description when read in conjunction with the accompanying drawings. Such embodiments, which are for illustrative purposes only, depict novel and non-obvious aspects of the invention. The drawings include the following figures:

[0084] FIG. 1 is a diagram of an example system configured to non-invasively assess dynamical properties of a physiological system to predict and/or estimate presence, non-presence, severity, and/or localization (where applicable) of disease or condition, or an indicator of one, in such physiological system, in accordance with an illustrative embodiment.

[0085] FIGS. 2A and 2B show examples of photoplethysmographic signal(s) acquired via measurement system of FIG. 1 in accordance with an illustrative embodiment.

[0086] FIG. 2C shows an example sensor configuration to acquire photoplethysmographic signal(s) of FIG. 2A in accordance with an illustrative embodiment.

[0087] FIG. 2D shows another example sensor configuration to acquire photoplethysmographic signal(s) of FIG. 2A in accordance with another illustrative embodiment.

[0088] FIGS. 2E and 2F each shows respective power spectral density of the photoplethysmographic signals of FIG. 2A with high frequency-noise removed.

[0089] FIG. 3A shows example cardiac signals (e.g., biopotential signals) as example biophysical signals acquired via the measurement system of FIG. 1, in accordance with an illustrative embodiment.

[0090] FIG. 3B is an example measurement system, such as that depicted in FIG. 1, configured to non-invasively measure biophysical signals to be used to assess dynamical properties of a physiological system to predict and/or estimate presence, non-presence, severity, and/or localization (where applicable) of disease or condition, or an indicator of one, in such physiological system, in accordance with an illustrative embodiment.

[0091] FIG. 3C shows an example use of the measurement system of FIG. 3B on a patient in a clinical setting in accordance with an illustrative embodiment.

[0092] FIG. 3D is a diagram of an example placement of the surface electrodes of the measurement system of FIG. 3B at the chest and back of a patient to acquire the cardiac signals of FIG. 3A in accordance with an illustrative embodiment.

[0093] FIG. 4A shows a three-dimensional phase space plot of photoplethysmographic signals acquired via an infrared sensor in accordance with an illustrative embodiment.

[0094] FIG. 4B shows a two-dimensional projection of the same data of FIG. 4A in accordance with an illustrative embodiment.

[0095] FIGS. 5A, 5B, and 5C illustrate example dynamical properties of synchronicity between acquired photoplethysmographic and cardiac signals in accordance with an illustrative embodiment.

[0096] FIG. 5D shows examples fitted ellipse features that can be extracted from the Poincar map of FIG. 5C in accordance with an illustrative embodiment.

[0097] FIG. 5E shows an example Poincar map of a data set acquired from a CAD-negative patient (i.e., a patient who does not have CAD).

[0098] FIG. 5F shows an example Poincar map of a data set acquired from a CAD-positive patient (i.e., a patient who has CAD in some form).

[0099] FIGS. 5G and 5H further shows crossover landmarks with respect to three cardiac signals acquired via a phase space recorder that can be used to trigger analysis of a photoplethysmographic signal in a Poincar maps in accordance with an illustrative embodiment.

[0100] FIGS. 5I, 5J, 5K, and 5L illustrate another example of dynamical properties of synchronicity between an acquired photoplethysmographic signal and cardiac signals in accordance with an illustrative embodiment.

[0101] FIGS. 6A, 6B, and 6C illustrate another set of example dynamical properties of synchronicity between acquired photoplethysmographic and cardiac signals in accordance with an illustrative embodiment.

[0102] FIG. 6D shows histograms and Poincar map results of a CAD-negative patient in accordance with an illustrative embodiment.

[0103] FIG. 6E shows histograms and Poincar map results of a CAD-positive patient in accordance with an illustrative embodiment.

[0104] FIGS. 7A, 7B, and 7C illustrate yet another example dynamical properties of synchronicity between acquired photoplethysmographic and cardiac signals in accordance with an illustrative embodiment.

[0105] FIG. 7D shows histogram-map and Poincar-map results of a CAD-negative patient in accordance with an illustrative embodiment.

[0106] FIG. 7E shows histogram-map and Poincar-map results of a CAD-positive patient in accordance with an illustrative embodiment.

[0107] FIGS. 7F, 7G, 7H, and 7I illustrate other example dynamical properties of synchronicity between acquired photoplethysmographic signal and cardiac signals in accordance with an illustrative embodiment.

[0108] FIGS. 8A, 8B, and 8C illustrate another set of example dynamical properties of synchronicity between acquired photoplethysmographic and cardiac signals in accordance with an illustrative embodiment.

[0109] FIG. 8D shows phase difference analysis of a CAD-negative patient generated between an acquired infrared photoplethysmographic signal and an acquired cardiac signal in accordance with an illustrative embodiment.

[0110] FIG. 8E shows a phase difference analysis of a CAD-positive patient generated between an acquired infrared photoplethysmographic signal and an acquired cardiac signal in accordance with an illustrative embodiment.

[0111] FIG. 9 shows experimental results from a study that indicates clinical predictive value of certain dynamical features extracted from Poincar and phase analyses of photoplethysmographic and cardiac signals that indicate the presence and non-presence of a disease or abnormal condition, or an indicator of one, in accordance with an illustrative embodiment.

[0112] FIGS. 10A and 10B, respectively, show ROC curves of a significant CAD classification using a trained Elastic Net model on two data sets in accordance with an illustrative embodiment.

[0113] FIGS. 10C and 10D, respectively, show ROC curves of an elevated or abnormal LVEDP classification using the trained XGBoost model on two data sets in accordance with an illustrative embodiment.

[0114] FIGS. 10E and 10F, respectively, shows ROC curves for the correct classification of presence of significant CAD and elevated or abnormal LVEDP using a subsequently trained Elastic Net model using only Poincar-map based features and a larger training data set in accordance with an illustrative embodiment.

[0115] FIGS. 10G and 10H, respectively, shows the feature contribution in the classifier models of FIGS. 10E and 10F for CAD and LVEDP classifications.

[0116] FIGS. 11A-11F show experimental results for a trained classifier to predict an elevated LVEDP in accordance with an illustrative embodiment.

[0117] FIG. 12 shows an example computing environment in which example embodiments of the analysis system and aspects thereof may be implemented.

DETAILED SPECIFICATION

[0118] Each and every feature described herein, and each and every combination of two or more of such features, is included within the scope of the present invention provided that the features included in such a combination are not mutually inconsistent.

[0119] While the present disclosure is directed to the beneficial assessment of biophysical signals, e.g., raw or pre-processed photoplethysmographic signals, cardiac signals, etc., in the diagnosis and treatment of cardiac-related pathologies and conditions, such assessment can be applied to the diagnosis and treatment (including, surgical, minimally invasive, and/or pharmacologic treatment) of any pathologies or conditions in which a biophysical signal is involved in any relevant system of a living body. In the cardiac (or cardiovascular) context, the assessment can be applied to the diagnosis and treatment of coronary artery disease (CAD) and diseases and/or conditions associated with an abnormal left ventricular end-diastolic pressure (LVEDP). The assessment can be applied for the diagnosis and treatment of any number of therapies, alone or in combination, such as the placement of a stent in a coronary artery, performance of an atherectomy, angioplasty, prescription of drug therapy, and/or the prescription of exercise, nutritional and other lifestyle changes, etc. Other cardiac-related pathologies or conditions that may be diagnosed include, e.g., arrhythmia, congestive heart failure, valve failure, pulmonary hypertension (e.g., pulmonary arterial hypertension, pulmonary hypertension due to left heart disease, pulmonary hypertension due to lung disease, pulmonary hypertension due to chronic blood clots, and pulmonary hypertension due to other disease such as blood or other disorders), as well as other cardiac-related pathologies, conditions and/or diseases. In some embodiments, the assessment may be applied to neurological-related pathologies and conditions. Non-limiting examples of neurological-related diseases, pathologies or conditions that may be diagnosed include, e.g., epilepsy, schizophrenia, Parkinson's Disease, Alzheimer's Disease (and all other forms of dementia), autism spectrum (including Asperger syndrome), attention deficit hyperactivity disorder, Huntington's Disease, muscular dystrophy, depression, bipolar disorder, brain/spinal cord tumors (malignant and benign), movement disorders, cognitive impairment, speech impairment, various psychoses, brain/spinal cord/nerve injury, chronic traumatic encephalopathy, cluster headaches, migraine headaches, neuropathy (in its various forms, including peripheral neuropathy), phantom limb/pain, chronic fatigue syndrome, acute and/or chronic pain (including back pain, failed back surgery syndrome, etc.), dyskinesia, anxiety disorders, conditions caused by infections or foreign agents (e.g., Lyme disease, encephalitis, rabies), narcolepsy and other sleep disorders, post-traumatic stress disorder, neurological conditions/effects related to stroke, aneurysms, hemorrhagic injury, etc., tinnitus and other hearing-related diseases/conditions and vision-related diseases/conditions.

[0120] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and/or discussion of such references is provided merely to clarify the description of the disclosed technology and is not an admission that any such reference is prior art to any aspects of the disclosed technology described herein. In terms of notation, [n] corresponds to the nth reference in the list. For example, [36] refers to the 36th reference in the list, namely F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., Scikit-learn: Machine learning in python, Journal of machine learning research 12, 2825-2830 (October 2011). All references cited and discussed in this specification are incorporated herein by reference in their entireties and to the same extent as if each reference was individually incorporated by reference.

[0121] Example System

[0122] FIG. 1 is a diagram of an example system 100 configured to non-invasively assess dynamical properties of a physiological system to predict and/or estimate (e.g., determine) presence, non-presence, localization, and/or severity of a disease or condition, or an indicator of one, in such physiological system, in accordance with an illustrative embodiment. Indeed, as used herein, the term predicting refers to forecasting a future event (e.g., potential development of a disease or condition), while the term estimating can refer to a quantification of some metric based on available information, e.g., for the presence, non-presence, localization, and/or severity of a disease or condition, or an indicator of one. The operations of predicting and estimating can be generally referred to as determining.

[0123] As noted herein, physiological systems can refer to the cardiovascular system, the pulmonary system, the renal system, the nervous system, and other functional systems and sub-systems of the body. In the context of the cardiovascular system, system 100 facilitates the investigation of complex, nonlinear dynamical properties of the heart over many heart cycles.

[0124] In FIG. 1, non-invasive measurement system 102 (shown as Measurement System 102) acquires two or more sets of biophysical signals 104 (shown as sets 104a and 104b) via measurement probes 106 (shown as probes 106a, 106b and probes 124a-124f) from a subject 108 (shown at location 108a and 108b) to produce a biophysical-signal data set 110, including a first type and a second type (shown as 110a and 110b).

[0125] The first type is acquired via probes 106a, 106b from the subject at location 108a (e.g., a finger of the subject) to generate a raw photoplethysmographic signal data set 110a from photoplethysmographic signal(s) 104a. In some embodiments, the raw photoplethysmographic signal data set 110a includes one or more photoplethysmographic signal(s) associated with measured changes in light absorption of oxygenated and/or deoxygenated hemoglobin.

[0126] The second type is acquired via probes 124a-124f from subject 108 to generate a cardiac signal data set 110b from cardiac signals 104b. In some embodiments, cardiac signal data set 110b includes data associated with biopotential signals acquired across a plurality of channels. In some embodiments, cardiac signal data set 110b includes wide-band biopotential signals, e.g., acquired via a phase-space recorder, as described in U.S. Patent Publication No. 2017/0119272, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In some embodiments, the cardiac signal data set includes bipolar wide-band biopotential signals, e.g., acquired via a phase-space recorder, as described in U.S. Patent Publication No. 2018/0249960, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In other embodiments, the cardiac signal data set 110b includes one or more biopotential signals acquired from conventional electrocardiogram (ECG/EKG) equipment (e.g., Holter device, 12 lead ECG, etc.).

[0127] Example Photoplethysmographic Signals

[0128] FIGS. 2A and 2B show examples of photoplethysmographic signal(s) 104a (also referred to herein as a PPG signal) acquired via measurement system 102 in accordance with an illustrative embodiment. Specifically, FIG. 2A shows a first signal waveform 302 comprising a red photoplethysmographic signal associated with the absorption level of the red spectrum of light by the deoxygenated hemoglobin from a finger of a patient. In some embodiments, the red photoplethysmographic signal has an associated wavelength that spans over 660 nm. FIG. 2A also shows a second signal waveform 304 (also referred to herein as red photoplethysmographic signal) of the absorption level of the infrared spectrum light (e.g., having wavelength that spans over 940 nm) by the oxygenated hemoglobin from a finger of a patient. Other spectra may be acquired as desired. In addition, measurements may be performed at other parts of the body. In FIG. 2A, the x-axis shows time (in seconds) and the y-axis shows the signal amplitude in millivolts (mv). FIG. 2B shows the signals of FIG. 2A over a larger time scale (x-axis) to include additional data in the waveform. FIG. 2C shows an example sensor configuration to acquire photoplethysmographic signal(s) 104a in accordance with an illustrative embodiment; other configurations are possible. In FIG. 2C, the transmissive system includes a light source (e.g., a red LED and an infrared LED) and a phototransistor (e.g., red detector and infrared detector); the phototransistor is distally located from the light source.

[0129] FIG. 2D shows another example sensor configuration to acquire photoplethysmographic signal(s) 104 in accordance with another illustrative embodiment. In FIG. 2D, the system also includes a light source (e.g., a red LED and an infrared LED) and a phototransistor (e.g., red detector and infrared detector); however, the phototransistor is proximally located to the light source to measure reflectance.

[0130] Photoplethysmography is used to optically measure variations of the volume of blood perfusing tissue (e.g., cutaneous, subcutaneous, cartilage) into which light is emitted, typically at a specific wavelength, from a LED or other source. The intensity of this light after passing through the tissue (e.g., fingertip, earlobe, etc.) is then registered by a photodetector to provide the photoplethysmographic signals. The amount of light absorbed depends on the volume of the blood perfusing the interrogated tissue. The variation in light absorbed is observable in the photoplethysmographic signal and can provide valuable information with regard to cardiac activity, pulmonary function, their interactions, and other physiological system functions [13].

[0131] In some embodiments, measurement system 102 comprises custom or dedicated equipment or circuitry (including off-the-shelf devices) that are configured to acquire such signal waveforms for the purpose diagnosing disease or abnormal conditions. In other embodiments, measurement system 102 comprises pulse oximeter or optical photoplethysmographic device that can output acquired raw signals for analysis. Indeed, in some embodiments, the acquired waveform 104 may be analyzed to calculate the level of oxygen saturation of the blood shown in FIG. 1 as SpO2 reading. For the exemplified analysis application however, only the waveform is processed and utilized.

[0132] FIGS. 2E and 2F each shows respective power spectral density of the photoplethysmographic signals of FIGS. 2A and 2B with high frequency-noise removed. In FIGS. 2E and 2F, the x-axis shows frequency (in Hertz) and the y-axis shows the log power of the signal.

[0133] Photoplethysmographic signal(s) 104 may be considered measurements of the state of a dynamical system in the body, similar to cardiac signals. The behavior of the dynamical system may be influenced by the actions of the cardiac and respiratory systems. It is postulated that any system aberrations (due, e.g., to a disease or abnormal condition) may manifest itself in the dynamics of photoplethysmographic signal(s) 104 via some interaction mechanism or mechanisms.

[0134] In some embodiments, the acquired photoplethysmographic signal(s) 104 are down-sampled to 250 Hz. Other frequency ranges may be used. In some embodiments, the acquired photoplethysmographic signal(s) 104 are processed to remove baseline wander and/or to filter for noise and/or mains frequencies.

[0135] The acquired photoplethysmographic signal(s) 104 may be embedded in some higher dimensional space (e.g., phase space embedding) to reconstruct the manifold (phase space) the underlying dynamical system creates. An example three-dimensional visualization and its two-dimensional projection of acquired photoplethysmographic signal(s) 104 (shown as 104c) are shown in FIGS. 4A and 4B, respectively. Specifically, FIG. 4A shows a 3D phase space plot of an acquired photoplethysmographic signal(s) 104 acquired via an infrared sensor. Axes are transformed voltage values. Colors are selected to show coherent structures within this geometric object. The dynamical features of the PPG are calculated based on the embedding represented by the figure. Description of embedding may be found in Sauer et al., Embedology, Jour. Of Statistical Physics, Vol. 65: 3-4, pp 579-616 (November 1991). FIG. 4B shows a two-dimensional projection of the same, and the same axes of FIG. 4A are shown.

[0136] Example Cardiac Signals

[0137] Electrocardiographic signals measure the action potentials of cardiac tissue (i.e., cardiomyocytes). There are various configurations of leads that can be used in a mammalian body, and in particular humans, to obtain these signals in the context of the present disclosure. In an example configuration, seven leads are used. This configuration results in three orthogonal channels/signals; e.g., X, Y and Z, corresponding to the coronal, sagittal and transverse planes, respectively.

[0138] As discussed above, in some embodiments, cardiac signal data set 110b includes data associated with biopotential signals acquired across a plurality of channels. In some embodiments, cardiac signal data set 110b includes wide-band biopotential signals, e.g., signals acquired via a phase-space recorder such as described in U.S. Patent Publication No. 2017/0119272, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In some embodiments, the cardiac signal data set includes bipolar wide-band biopotential signals, e.g., acquired via a phase-space recorder such as described in U.S. Patent Publication No. 2018/0249960, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In other embodiments, the cardiac signal data set 110b includes one or more biopotential signals acquired from conventional electrocardiogram (ECG/EKG) equipment (e.g., Holter device, 12 lead ECG, etc.).

[0139] The phase space recorder as described in 2017/0119272, in some embodiments, is configured to concurrently acquire photoplethysmographic signals 104a along with cardiac signal 104b. Thus, in some embodiments, measurement system 102b is configured to acquire two types of biophysical signals.

[0140] FIG. 3A shows example cardiac signals (e.g., biopotential signals) as example biophysical signals acquired via the measurement system of FIG. 1, in accordance with an illustrative embodiment. The signals are shown with baseline wander and high-frequency noise removed. In some embodiments, the cardiac signals 104b are acquired using a phase space recorder device, e.g., such as described in U.S. Patent Publication No. 2017/0119272. The signals 104b includes bipolar biopotential measurements acquired over three channels to provide three signals 302, 304, 306 (also referred to channel x, channel y, and channel z or the coronal, sagittal and transverse planes, respectively). In FIG. 3A, the x-axis shows time (in seconds) and the y-axis shows the signal amplitude in millivolts (my).

[0141] FIG. 3B is a diagram of a phase space recorder device, e.g., as described in U.S. Patent Publication No. 2017/0119272, configured to acquire cardiac signals 104b. The phase space recorder device is further configured to also acquire photoplethysmographic signals 104a. FIG. 3C shows an example placement of the measurement system of FIG. 3B on a human patient.

[0142] Referring still to FIG. 1B, non-invasive measurement system 102b is configured to transmit, e.g., over a communication system and/or network, or over direct connection, the acquired cardiac-signal data set 110b, or a data set derived or processed therefrom, to repository 112 (e.g., a storage area network) that is accessible to a non-invasive biophysical-signal assessment system. The non-invasive biophysical-signal assessment system 114 (shown as analytic engine 114) is configured to analyzed dynamical properties of the acquired photoplethysmographic signal(s).

[0143] In the cardiac and/or electrocardiography contexts, measurement system 102 is configured to capture cardiac-related biopotential or electrophysiological signals of a mammalian subject (such as a human) as a biopotential cardiac signal data set. In some embodiments, measurement system 102 is configured to acquire a wide-band cardiac phase gradient signals as a biopotential signal, a current signal, an impedance signal, a magnetic signal, an ultrasound or acoustic signal, etc. The term wide-band in reference to an acquired signal, and its corresponding data set, refers to the signal having a frequency range that is substantially greater than the Nyquist sampling rate of the highest dominant frequency of a physiological system of interest. For cardiac signals, which typically has a dominant frequency components between about 0.5 Hz and about 80 Hz, the wide-band cardiac phase gradient signals or wide-band cardiac biophysical signals comprise cardiac frequency information at a frequency selected from the group consisting between about 0.1 Hz and 1 KHz, between about 0.1 Hz and about 2 KHz, between about 0.1 Hz and about 3 KHz, between about 0.1 Hz and about 4 KHz, between about 0.1 Hz and about 5 KHz, between about 0.1 Hz and about 6 KHz, between about 0.1 Hz and about 7 KHz, between about 0.1 Hz and about 8 KHz, between about 0.1 Hz and about 9 KHz, between about 0.1 Hz and about 10 KHz, and between about 0.1 Hz and greater than 10 KHz (e.g., 0.1 Hz to 50 KHz or 0.1 Hz to 500 KHz). In addition to capturing the dominant frequency components, the wide-band acquisition also facilitates capture of other frequencies of interest. Examples of such frequencies of interest can include QRS frequency profiles (which can have frequency ranges up to 250 Hz), among others. The term phase gradient in reference to an acquired signal, and corresponding data set, refers to the signal being acquired at different vantage points of the body to observe phase information for a set of distinct events/functions of the physiological system of interest. Following the signal acquisition, the term phase gradient refers to the preservation of phase information via use of non-distorting signal processing and pre-processing hardware, software, and techniques (e.g., phase-linear filters and signal-processing operators and/or algorithms).

[0144] In some embodiments, cardiac signal data set 110b includes wide-band biopotential signals, such as, e.g., those acquired via a phase-space recorder as described in U.S. Patent Publication No. 2017/0119272, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In some embodiments, the cardiac signal data set includes bipolar wide-band biopotential signals, e.g., acquired via a phase-space recorder such as described in U.S. Patent Publication No. 2018/0249960, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In other embodiments, the cardiac signal data set 110b includes one or more biopotential signals acquired from conventional electrocardiogram (ECG/EKG) equipment (e.g., Holter device, 12 lead ECG, etc.).

[0145] The phase space recorder as described in U.S. Patent Publication No. 2017/0119272, in some embodiments, is configured to concurrently acquire photoplethysmographic signals 104a along with cardiac signal 104b. Thus, in some embodiments, measurement system 102b is configured to acquire two types of biophysical signals.

[0146] In the neurological context, measurement system 102 is configured to capture neurological-related biopotential or electrophysiological signals of a mammalian subject (such as a human) as a neurological biophysical-signal data set. In some embodiments, \ measurement system 102 is configured to acquire wide-band neurological phase gradient signals as a biopotential signal, a current signal, an impedance signal, a magnetic signal, an ultrasound or acoustic signal, an optical signal, etc. An example of measurement system 102 is described in U.S. Patent Publication No. 2017/0119272 and in U.S. Patent Publication No. 2018/0249960, which is incorporated by reference herein in its entirety.

[0147] In some embodiments, the measurement system 102 is configured to capture wide-band biopotential biophysical phase gradient signals as unfiltered mammalian electrophysiological signals such that the spectral component(s) of the signals are not altered. Indeed, in such embodiments, the wide-band biopotential biophysical phase gradient signals are captured, converted, and even analyzed without having been filtered (via, e.g., hardware circuitry and/or digital signal processing techniques, etc.) (e.g., prior to digitization) that otherwise can affect the phase linearity of the biophysical signal of interest. In some embodiments, the wide-band biopotential biophysical phase gradient signals are captured in microvolt or sub-microvolt resolutions that are at, below, or significantly below, the noise floor of conventional electrocardiographic, electroencephalographic, and other biophysical-signal acquisition instruments. In some embodiments, the wide-band biopotential biophysical signals are simultaneously sampled having a temporal skew or lag of less than about 1 microsecond, and in other embodiments, having a temporal skew or lag of not more than about 10 femtoseconds. Notably, the exemplified embodiments minimize non-linear distortions (e.g., those that can be introduced via certain filters) in the acquired wide-band phase gradient signal to not affect the information therein.

[0148] FIG. 3D is a diagram of an example placement of the surface electrodes 106a-106g at a patient to acquire the cardiac signals of FIG. 3A in accordance with an illustrative embodiment. Specifically, FIG. 3D shows example placement of the surface electrodes 106a-106g at the chest and back of a patient to acquire bio-potential signals associated with wide-band cardiac phase gradient signals in accordance with an illustrative embodiment. In the left pane of FIG. 3D, surface electrodes 106a-106g are shown placed at the chest and back area of the patient. In the right pane of FIG. 3D, side view of placement of the surface electrodes 106a-106g is shown.

[0149] In the example configuration shown in FIG. 3D, surface electrodes 106a-106g are positioned on the patient's skin at i) a first location proximal to a right anterior axillary line corresponding to a 5th intercostal space; ii) a second location proximal to a left anterior axillary line corresponding to the 5th intercostal space; iii) a third location proximal to a left sternal border corresponding to a 1st intercostal space; iv) a fourth location proximal to the left sternal border below the sternum and lateral to the patient's xiphoid process; v) a fifth location proximal to the left sternal border corresponding to a 3rd intercostal space; vi) a sixth location proximal to the patient's back directly opposite of the fifth location and left of the patient's spine; and viii) a seventh location proximal to a right upper quadrant corresponding to a 2nd intercostal space along a left axillary line. A common lead (shown as CMM) is also shown. Locations of individual surface electrodes may vary in other embodiments of the present disclosure as other electrode configurations may be useful.

[0150] Referring to FIG. 1, non-invasive measurement system 102 is configured with circuitry and computing hardware, software, firmware, middleware, etc. to acquire both the cardiac signal and the photoplethysmographic signal to generate the biophysical-signal data set 110. In other embodiments, non-invasive measurement system 102 includes a first equipment (not shown) to acquire the cardiac signal and includes a second equipment (not shown) to acquire the photoplethysmographic signal.

[0151] Referring still to FIG. 1, non-invasive measurement system 102 is configured to transmit, e.g., over a communication system and/or network, or over direct connection, the acquired biophysical-signal data set 110, or a data set derived or processed therefrom, to a repository 112 (e.g., a storage area network) (not shown) that is accessible to a non-invasive biophysical-signal assessment system. The non-invasive biophysical-signal assessment system 114 (shown as analytic engine 114) is configured to, e.g., analyzed dynamical properties of the acquired photoplethysmographic signal(s).

[0152] In some embodiments, analytic engine 114 includes a machine learning module 116 configured to assess a set of features determined via one or more feature extraction modules (e.g. 118, 120) from the acquired biophysical signal(s) to determine features of clinical significance. Once the features have been extracted from the PPG signal(s) or cardiac signal(s), then any type of machine learning can be used. Examples of embodiments of machine learning module 116 is configured to implement, but not limited to, decision trees, random forests, SVMs, neural networks, linear models, Gaussian processes, nearest neighbor, SVMs, Nave Bayes. In some embodiment, machine learning module 116 may be implemented, e.g., as described in U.S. patent application Ser. No. 15/653,433, entitled Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions; and U.S. patent application Ser. No. 15/653,431, entitled Discovering Genomes to Use in Machine Learning Techniques; each of which are incorporated by reference herein in its entirety. The photoplethysmographic signal(s) may be combined with other acquired photoplethysmographic signal(s) to be used in a training data set or validation data set for the machine learning module 116 in the evaluation of a set of assessed dynamical features. The photoplethysmographic signal(s) may have an associated label 122 for a given disease state or abnormal condition, or an indicator of one. If determined to be of clinical significance, an assessed dynamical features (e.g., from 118 or 120) may be subsequently used as a predictor for the given disease or abnormal condition, or an indicator of one.

[0153] In some embodiments, analytic engine 114 includes a pre-processing module, e.g., configured to normalize and/or remove baseline wander from the acquired photoplethysmographic signal(s).

[0154] In some embodiments, system 100 includes a healthcare provider portal to display, e.g., in a report, score or various outputs of the analytic engine 114 in predicting and/or estimating presence, non-presence, severity, and/or localization (where applicable) of a disease or abnormal condition, or an indicator of one. The physician or clinician portal, in some embodiments, is configured to access and retrieve reports from a repository (e.g., a storage area network). The physician or clinician portal and/or repository can be compliant with various privacy laws and regulations such as the U.S. Health Insurance Portability and Accountability act of 1996 (HIPAA). Further description of an example healthcare provider portal is provided in U.S. Pat. No. 10,292,596, entitled Method and System for Visualization of Heart Tissue at Risk, which is incorporated by reference herein in its entirety. Although in certain embodiments, the portal is configured for presentation of patient medical information to healthcare professionals, in other embodiments, the healthcare provider portal can be made accessible to patients, researchers, academics, and/or other portal users.

[0155] Synchronicity Evaluation Between Cardiac Signal and Raw Photoplethysmographic Signals

[0156] Referring still to FIG. 1, the dynamical feature extraction module 118, in some embodiments, is configured to evaluate one or more nonlinear dynamical properties of synchronicity between one or more of acquired photoplethysmographic signal(s) 104a and one or more of the acquired cardiac signal 104b. Several examples of synchronicity are disclosed.

[0157] The electrophysiological activity of the heart is a nonlinear process which in conjunction with the myocytes' mechano-electrical feedback produces very complex nonlinear responses [26]. These behaviors whether normal (reaction to extrinsic conditions) or due to a disease can be studied and characterized using nonlinear statistics related to the nonlinear dynamics and chaoticity of the heart. Synchronicity features that are based on dynamics observed in cardiac and photoplethysmographic signals may encode the health state of the heart and are used to train a machine learning model for prediction for various disease state of condition.

[0158] In a Poincar map, the mapping X.sub.n+1=P(X.sub.n) may be defined using triggers (e.g., intersection with E), and the set of Poincar points {X.sub.0, X.sub.1, . . . , X.sub.n} can then be analyzed geometrically and/or statistically to deduce more information about the system.

[0159] Synchronicity Features Example PM #1

[0160] FIGS. 5A, 5B, and 5C illustrate example dynamical properties of synchronicity between acquired photoplethysmographic signal and cardiac signals in accordance with an illustrative embodiment. Specifically, in FIGS. 5A, 5B, and 5C, synchronicity is determined, via histogram/Poincar map analysis, using landmarks defined by the photoplethysmographic signals.

[0161] Specifically, FIG. 5A shows, in a first plot 502, cross-over landmarks 504a and 504b defined between a red photoplethysmographic signal 302 and an infrared photoplethysmographic signal 304. A second plot 506 shows the cross-over landmarks 504a and 504b in relation to one of the channels of the acquired cardiac signal 104b. In FIG. 5A, the x-axis shows time (in seconds) and the y-axis shows the signal amplitude in millivolts (my).

[0162] FIG. 5B shows a histogram of the distribution of values of the cardiac signal 104b at the cross-over landmarks 504a and 504b. Specifically, FIG. 5B shows distributions 508, 510 corresponding to amplitude values of the cardiac signal at the respective first and second sets of crossover landmarks (504a, 504b). In FIG. 5B, the x-axis of the histogram shows signal amplitude (in mV) and the y-axis shows the frequency/count.

[0163] In some embodiments, dynamical feature extraction module 118 is configured to generate a histogram (e.g., as generated per FIG. 5B) and extract statistical and geometric properties from the generated histogram. In some embodiments, the extracted histogram features include, for example, but not limited to, modes, standard derivation, skewness, kurtosis, and mutual information. Mode refers to the set of data values that appear most often in a date set. Skewness refers to a measure of the asymmetry of the probability distribution of the data set about its mean. Kurtosis refers to the sharpness of the peak of a distribution curve. In some embodiments, mutual information is used to quantify the probabilistic dependence of the information in the acquired signals and is determined by first calculating a probability normalization of the histogram of each time series and then constructing and normalization a 2-dimensional histogram of the two time-series. The mutual information I(X, Y) between two random variables X and Y can be the amount of reduction in the uncertainty of one random variable, say, X given another variable Y defined per Equation 1.

[00001]$\begin{array}{cc}I\left(X,Y\right)=\underset{yY}{.Math.}.Math.\underset{xX}{.Math.}.Math.p\left(x,y\right).Math.\log \left(\frac{p\left(x,y\right)}{p\left(x\right).Math.p\left(y\right)}\right)& \left(\mathrm{Equation}.Math..Math.1\right)\end{array}$

[0164] In Equation 5, p(., .) is the probability distribution over the specified variables.

[0165] FIG. 5C shows a Poincar map 512 of values of the cardiac signal 104b at the crossover landmarks 504a and 504b. That is, the Poincar map records the value of the cardiac signal 104b, or at least one of the channels, based on triggers defined by the crossover landmarks 504a, 504b. In FIG. 5C, the x-axis and y-axis each shows the difference in amplitude values for the cardiac signal from cycles to cycles.

[0166] In some embodiments, to generate the Poincar map 512, the system plots/generates a 2D pairs of points [x.sub.i, x.sub.i+1] (e.g., (x.sub.i, x.sub.2), (x.sub.2, x.sub.3), etc.) against the points [x.sub.i, x.sub.i1] (e.g., (x.sub.0, x.sub.1), (x.sub.1, x.sub.2), etc.) of the amplitude values of a cardiac signal at the cross-over landmark points formed between photoplethysmographic signals.

[0167] In some embodiments, dynamical feature extraction module 118 is configured to generate Poincar map 512. Following generation of Poincar map 512, dynamical feature extraction module 118, in some embodiments, is configured to generate a geometric object from the map data. In FIG. 5C, in some embodiments, dynamical feature extraction module 118 determines an ellipse 511 based on an ellipse fit operation of the data associated with a cluster (e.g., 510a). Based on the fitted ellipse, dynamical feature extraction module 118, in some embodiments, is configured to determine geometric parameters such as, but not limited to, length of semi axis a (514), semi axis b (516), length along a long axis (518), and length along a short axis (520) as shown in FIG. 5D.

[0168] The dynamical feature extraction module 118, in some embodiments, may extract other parameters such as void area, surface area, porosity, perimeter length, density, among others.

[0169] Indeed, synchronicity between acquired photoplethysmographic signals (e.g., where acquired raw signals are merely processed to remove baseline wander and high frequency noise) and a cardiac signal based on triggers defined in the photoplethysmographic signal may be used to assess for the presence, non-presence, severity, and/or localization (where applicable) of coronary artery disease (CAD), pulmonary hypertension, heart failure in various forms, among other diseases and conditions. In the CAD context, FIG. 5E shows an example Poincar map of a data set acquired from a CAD-negative patient (i.e., a patient who does not have CAD). FIG. 5F shows an example Poincar map of a data set acquired from a CAD-positive patient (i.e., a patient who has CAD in some form). It is readily apparent that the Poincar maps of FIGS. 5E and 5F between a CAD-negative patient and a CAD-positive patient are different. FIGS. 5E and 5F shows an example Poincar map generated from the amplitude values of a cardiac signal at landmarks defined by the photoplethysmographic signals. In the x- and y-axis, the Poincar map shows the signal amplitude of the cardiac signal (e.g., as normalized with high-frequency and baseline wander removed) at a first index x-1 and a second index x in the x-axis and the second index x and third index x+1 in the y-axis. Indeed, in a Poincar map, time and data positions as denoted by an index value are synonymous are used interchangeably herein. Additionally, other index or time increments may be used. That is, each assessed parameter (e.g., signal amplitude) at a given time/data point is shown in the Poincar map with respect to the next time/data point (e.g., [x.sub.i1, x] versus [x, x.sub.i+1]). The Poincar map thus facilitates the analysis of variability of a given parameter (e.g., variability in the lowest peak landmarks) between cycles in the acquired data set. Similar analysis may be applied to any of the parameters and features discussed herein.

[0170] FIGS. 5G and 5H further show crossover landmarks (504a, 504b) with respect to three cardiac signals acquired via a phase space recorder that can be used to trigger analysis of a photoplethysmographic signal in a Poincar maps. Description of an example phase space recorder and its associated probe locations are described, for example, in U.S. Patent Publication No. 2018/0249960, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition, which is incorporated by reference herein in its entirety. In FIG. 5G, the data set is shown for a CAD-negative patient. In FIG. 5H, the data set is shown for a CAD-positive patient.

[0171] Specifically, in FIG. 5G, it is observed that the crossover landmarks from the photoplethysmographic signals generally corresponds to the P-waves (522) and T-waves (524) of cardiac signals 104b (shown as 104b channel x, 104b channel y, and 104b channel z). P-waves generally correspond to atrial depolarization associated with atrial contraction and atrial systole. T-waves generally correspond to ventricular repolarization.

[0172] In contrast, as shown in FIG. 5H, for CAD-positive patients, it can be observed that the crossover landmarks from the photoplethysmographic signals has shifted with respect to the P-waves and T-waves. The Poincar map (e.g., as described in relation to FIG. 5C) facilities the quantification, and in some embodiments, visualization, of this resulting shift which is understood to be condition of a diseasehere, coronary artery disease or CAD.

[0173] In FIGS. 5G and 5H, the amplitude value of a cardiac signal at landmarks in the photoplethysmographic signals are plotted in pairs (e.g., at a first time x1 and a second time x in the x-axis and at second time x and a third time x+1 in the y-axis).

[0174] FIGS. 5I, 5J, 5K, and 5L illustrate another example of dynamical properties of synchronicity between an acquired photoplethysmographic signal and cardiac signals in accordance with an illustrative embodiment. Specifically, FIG. 5I show a histogram of a distribution of P.sub.x1 and P.sub.x2 Poincar points, e.g., as shown in FIG. 5A, for a healthy patient (i.e., CAD negative patient), and FIG. 5J shows a Poincar map of the same data along with an ellipse fit to geometrically characterize the data distribution. FIG. 5K show a histogram of a distribution of P.sub.x1 and P.sub.x2 Poincar points, e.g., as shown in FIG. 5A, for an unhealthy patient (i.e., CAD positive patient), and FIG. 5L shows a corresponding Poincar map and ellipse fit of the same.

[0175] Statistical properties of these distributions (e.g., mean, median, deviation, kurtosis etc.) and the geometrical properties of the encompassing ellipse (e.g., major and minor diameters and tilt) may be computed and used as features.

[0176] Table 1 provides a list of example synchronicity feature extracted parameters associated with Poincar map analysis PM #1 as their corresponding description.

TABLE-US-00001 TABLE 1 Parameter name Description dXDmj Major diameter of ellipse from Poincar map PM#1 for the PSR/ECG X channel. dXDmn Minor diameter of ellipse from Poincar map PM#1 for the PSR/ECG X channel. dZDmn Minor diameter of ellipse from Poincar map analysis PM#1 on the PSR/ECG Z channel. dYAlpha Tilt angle, alpha, of the ellipse from Poincar map analysis PM#1 on the PSR/ECG Y channel. dZAlpha Tilt angle, alpha, of ellipse from Poincar map analysis PM#1 on the PSR/ECG Z channel. dXMean1 Amplitude mean of the PSR/ECG X channel at the first intersection/crossover points of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dXStd1 Standard deviation of the distribution of the PSR/ECG X channel triggered by the first crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dXStd2 Standard deviation of the distribution of the PSR/ECG X channel triggered by second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dYStd2 Standard deviation of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dYKurt2 Kurtosis of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dZKurt2 Kurtosis of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dYMode2 Mode of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dZMode2 Mode of distribution of the PSR/ECG Z channel data triggered at the second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dZSkew1 Kurtosis of distribution of the PSR/ECG Z channel data triggered at first crossovers of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dZSkew2 Kurtosis of distribution of the PSR/ECG Z channel data triggered at second crossovers of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dYRelStdMAD2 Relative difference between the standard deviation and median absolute deviation (MAD) of distribution of the PSR/ECG Y channel data triggered at second crossovers of photoplethysmographic signals (e.g., in Poincar map analysis PM#1). dZRelStdMAD1 Relative difference between the standard deviation and median absolute deviation (MAD) of distribution of the PSR/ECG Z channel data triggered at first crossovers of photoplethysmographic signals (e.g., in Poincar map analysis PM#1).

[0177] Synchronicity Features Example PM #2

[0178] FIGS. 6A, 6B, and 6C illustrate another set of example dynamical properties of synchronicity between acquired photoplethysmographic signals and cardiac signals in accordance with an illustrative embodiment. In FIGS. 6A, 6B, and 6C, synchronicity is determined, via histogram/Poincar map analysis, using landmarks in a cardiac signal.

[0179] FIG. 6A shows one channel of an acquired cardiac signal data set with respect to an acquired red photoplethysmographic signal 302 and infrared photoplethysmographic signal 304. The x-axis shows the time domain (in index count of the data set) and the y-axis shows the acquired amplitude of the signal in millivolts;

[0180] FIG. 6B shows a histogram 604 of the amplitude magnitude values of one of the photoplethysmographic signal at an assessed peak of the QRS waveform (also referred to as R-peak) of the cardiac signal. Here, the magnitude of the infrared photoplethysmographic signal is shown. In other embodiments, both amplitude magnitudes of the red and infrared photoplethysmographic signals are recorded and analyzed for statistical and geometric features. In FIG. 6B, the x-axis of the histogram shows signal amplitude (in mV) and the y-axis shows the frequency/count.

[0181] In some embodiments, the dynamical feature extraction module 118 is configured to generate a histogram and extract statistical properties, such as, but not limited to modes, scale, skewness, kurtosis, and mutual information, from the generated histogram, e.g., as discussed in relation to FIG. 5B.

[0182] FIG. 6C shows a Poincar map 606 of amplitude magnitude values of the acquired infrared photoplethysmographic signal 304 at an assessed R-peak of the acquired cardiac signal. In FIG. 6C, the amplitude values of a photoplethysmographic signal at landmarks in a cardiac signal is plotted in pair (e.g., at a first time x-1 and a second time x in the x-axis and at the second time x and a third time x+1 in the y-axis).

[0183] Following generation of the Poincar map 606, the dynamical feature extraction module 118, in some embodiments, is configured to generate a geometric object from the data. In FIG. 6C, the dynamical feature extraction module 118 determines an ellipse 611 based on an ellipse fit operation of the data associated with a cluster (e.g., 510a). Based on the fitted ellipse, the dynamical feature extraction module 118, in some embodiments, is configured to determine geometric parameters such as length of semi axis a (514), semi axis b (516), length along a long axis (518), and length along a short axis (520), e.g., as described in relation to FIG. 5D. The dynamical feature extraction module 118, in some embodiments, may extract other parameters such as void area, surface area, porosity, perimeter length, density, among others.

[0184] Indeed, synchronicity between acquired raw photoplethysmographic signal and cardiac signal based on triggers defined in the cardiac signal may be used to assess for presence, non-presence, severity, and/or localization of coronary artery disease, pulmonary hypertension, heart failure, among other disease, conditions, and associated conditions.

[0185] In some embodiments, to generate Poincar map 512, module 118 plots/generates a 2D pairs of points [x.sub.i, x.sub.i+1] (e.g., (x.sub.1, x.sub.2), (x.sub.2, x.sub.3) etc.) against the points [x.sub.i1, x.sub.i] (e.g., (x.sub.0, x.sub.1), (x.sub.1, x.sub.2)) of the amplitude values of a given photoplethysmographic signal (e.g., the red or the infrared photoplethysmographic signal) at a landmark of a cardiac signal (e.g., at one of channel x, y, or z).

[0186] FIG. 6D shows histograms and Poincar map results of a CAD-negative patient in accordance with an illustrative embodiment. Specifically, FIG. 6D shows a histogram 608 and Poincar map 610 of a CAD-negative patient generated from amplitude magnitude values of an acquired red photoplethysmographic signal at R-peaks of one of an acquired cardiac signal 104b and histogram 612 and Poincar map 614 generated from amplitude magnitude values of an acquired infrared photoplethysmographic signal at R-peaks of one of an acquired cardiac signal 104b. In the histograms 608, 612, the x-axis of the histogram shows signal amplitude (in mV) and the y-axis shows the frequency/count. In the Poincar maps 610, 610a, 614, 614a, the x-axis and the y-axis each shows the amplitude values of a photoplethysmographic signal at landmarks in a cardiac signal as plotted in pair (e.g., at a first time x1 and a second time x in the x-axis and at the second time x and a third time x+1 in the y-axis). Plotting can also be done with respect to index values denoted for a given data set.

[0187] FIG. 6E shows histograms and Poincar map results of a CAD-positive patient in accordance with an illustrative embodiment. Specifically, FIG. 6E shows histogram 616 and Poincar map 618 of a CAD-positive patient generated from amplitude magnitude values of an acquired red photoplethysmographic signal at R-peaks of one of an acquired cardiac signal 104b and histogram 620 and Poincar map 622 generated from amplitude magnitude values of an acquired infrared photoplethysmographic signal at R-peaks of one of an acquired cardiac signal 104b. In the histograms 616, 620, the x-axis of the histogram shows signal amplitude (in mV) and the y-axis shows the frequency/count. In the Poincar maps 618, 618a, 622, 622a, the x-axis and the y-axis each shows the amplitude values of a photoplethysmographic signal at landmarks in a cardiac signal as plotted in pair (e.g., at a first time x1 and a second time x in the x-axis and at the first time x and a third time x+1 in the y-axis). Plotting can also be done with respect to index values denoted for a given data set.

[0188] Table 2 provides a list of example synchronicity feature extracted parameters associated with Poincar map analysis PM #2 as their corresponding description.

TABLE-US-00002 TABLE 2 dDmjL Major diameter of the ellipse in Poincare map derived from amplitude of infrared photo-photoplethysmographic signal at R-peaks of a cardiac signal (e.g., per example Poincar map analysis PM#2). dDmjU Major diameter of the ellipse in Poincare map derived from amplitude of red photo-photoplethysmographic signal at R-peaks of a cardiac signal (e.g., per example Poincar map analysis PM#2). dDmnU Minor diameter of the ellipse in Poincare map derived from amplitude of red photo-photoplethysmographic signal at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM#2). dAlphaL Tilt angle, alpha, of the ellipse in Poincare map derived from amplitude of the infrared photo-photoplethysmographic signal at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM#2). dAlphaU Tilt angle, alpha, of the ellipse in Poincare map derived from amplitude of the red photo-photoplethysmographic signal at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM#2). dKurtL Kurtosis of histogram of Poincar map analysis PM2 of infrared photoplethysmographic signal. dMeanL Mean value of histogram of Poincar map analysis PM2 of infrared photoplethysmographic signal. dMeanU Mean value of histogram of Poincar map analysis PM#2 for red photoplethysmographic signal. dModeLP Mode of the distribution (histogram) of Poincar map analysis PM#2 for infrared photoplethysmographic signal. dModeUP Mode of the distribution (histogram) of Poincar map analysis PM#2 for infrared photoplethysmographic signal. dStdU Standard deviation of histogram of Poincar map analysis PM#2 for the red photoplethysmographic signal.

[0189] Synchronicity Features Example PM #3

[0190] FIGS. 7A, 7B, and 7C illustrate yet other example dynamical properties of synchronicity between acquired photoplethysmographic signal and cardiac signals in accordance with an illustrative embodiment. In FIGS. 7A, 7B, and 7C, synchronicity is determined using, via histogram/Poincar map analysis, phase relations between landmarks in one or more of the cardiac signal and in one or more of the photoplethysmographic signals.

[0191] Specifically, FIG. 7A shows, in a first plot 702, crossover landmarks 504a and 504b as described in relation to FIG. 5A are defined between a red photoplethysmographic signal 302 and an infrared photoplethysmographic signal 304. A second plot 704 shows crossover landmarks 504a and 504b in relation to one of the channels of the acquired cardiac signal 104b. Plot 704 further shows a phase difference R-peak 602 (shown as TP 706 and TT 708) of the cardiac signal 104b and the crossover landmarks 504a and 504b of the photoplethysmographic signals. The x-axis shows the time domain (in index count of the data set) and the y-axis shows the acquired amplitude of the signal in millivolts;

[0192] FIG. 7B shows a histogram of the distribution of phase relations between the cardiac signal 104b and the respective crossover landmarks 504a and 504b. Specifically, FIG. 7B shows distributions 710, 712 corresponding to phase relations between R-peak of the cardiac signal and the first and second sets of crossover landmarks (504a, 504b). In FIG. 7B, the x-axis of the histogram shows signal amplitude (in bin number) and the y-axis shows the frequency/count.

[0193] In some embodiments, dynamical feature extraction module 118 is configured to generate a histogram (e.g., as generated per FIG. 5B) and extract statistical and geometric properties from the generated histogram. In some embodiments, the extracted histogram features include, for example, but not limited to, modes, standard deviation, skewness, kurtosis, and mutual information. The term mode as used herein refers to the set of data values that appear most often in a date set. The term skewness as used herein refers to a measure of the asymmetry of the probability distribution of the data set about its mean. The term kurtosis as used herein refers to the sharpness of the peak of a distribution curve.

[0194] FIG. 7C shows a Poincar map 714 of phase relations between the cardiac signal 104b and the respective crossover landmarks 504a and 504b. In FIG. 7C, the time values of TP interval and TT intervals defined between the photoplethysmographic signals and the cardiac signals are plotted in pair (e.g., at a first time x-1 and a second time x in the x-axis and at the second time x and a third time x+1 in the y-axis).

[0195] In FIG. 7C, the amplitude values (e.g., in bits) of a photoplethysmographic signal at landmarks in a cardiac signal is plotted in pair (e.g., at a first time x1 and a second time x in the x-axis and at the second time x and a third time x+1 in the y-axis).

[0196] That is, to generate the Poincar map 714, the system plots/generates a 2D pairs of points [x.sub.i, x.sub.i+1] (e.g., (x.sub.1, x.sub.2), (x.sub.2, x.sub.3) etc.) of the TP interval index/time against the points [x.sub.i, x.sub.i+1] (e.g., (x.sub.1, x.sub.2), (x.sub.2, x.sub.3) etc.) of the TT interval index/time.

[0197] In some embodiments, dynamical feature extraction module 118 is configured to generate Poincar map 714. Following generation of Poincar map 714, the dynamical feature extraction module 118, in some embodiments, is configured to generate a geometric object from the map data. In FIG. 7C, in some embodiments, dynamical feature extraction module 118 determines an ellipse 716 based on an ellipse fit operation of the data associated with a cluster (e.g., 712a). Based on the fitted ellipse, the dynamical feature extraction module 118, in some embodiments, is configured to determine geometric parameters such as, but not limited to, length of semi axis a (514), semi axis b (516), length along a long axis (518), and length along a short axis (520) as shown in FIG. 5D.

[0198] The dynamical feature extraction module 118, in some embodiments, may extract other parameters such as void area, surface area, porosity, perimeter length, density, among others.

[0199] Indeed, synchronicity between one or more acquired raw photoplethysmographic signals and one or more cardiac signals based on phase relations between landmarks in the photoplethysmographic signals and in the cardiac signal may be used to assess for presence, non-presence, severity, and/or localization (where applicable) of coronary artery disease, pulmonary hypertension, heart failure, among other disease, conditions, and associated conditions. FIG. 7D shows an example Poincar map of a data set acquired from a CAD-negative patient. FIG. 7E shows an example Poincar map of a data set acquired from a CAD-positive patient.

[0200] FIG. 7D shows histogram- and Poincar-map results of a CAD-negative patient in accordance with an illustrative embodiment. Specifically, FIG. 7D shows histogram 716 and Poincar map 718 of a CAD-negative patient generated from phase relations (e.g., TP and TT) between an acquired infrared photoplethysmographic signal and R-peaks of one of an acquired cardiac signal 104b.

[0201] FIG. 7E shows histogram- and Poincar-map results of a CAD-positive patient in accordance with an illustrative embodiment. Specifically, FIG. 7E shows histogram 720 and Poincar map 722 of a CAD-positive patient generated from phase relations (e.g., based on TP and TT intervals) between an acquired infrared photoplethysmographic signal and R-peaks of one of an acquired cardiac signal 104b. Poincar maps 718a and 722a further shows fitted ellipses in respective plots 718 and 722.

[0202] FIGS. 7F, 7G, 7H, and 7I illustrate other example dynamical properties of synchronicity between acquired photoplethysmographic signal and cardiac signals in accordance with an illustrative embodiment. Specifically, FIG. 7F is a histogram showing the distribution of time intervals (for the time intervals as shown in FIG. 7A) between pre-defined landmarks in a photoplethysmographic signal and a cardiac signal for a healthy patient (i.e., CAD-negative patient), and FIG. 7G shows a Poincar map of the same data along with an ellipse fit to geometrically characterize the data distribution. FIG. 7H is a histogram of showing the distribution of time intervals (for the time intervals as shown in FIG. 7A) between pre-defined landmarks in a photoplethysmographic signal and a cardiac signal for an unhealthy patient (i.e., CAD-positive patient), and FIG. 7I shows a corresponding Poincar map and ellipse fit of the same.

[0203] Table 3 provides a list of example synchronicity feature extracted parameters associated with Poincar map analysis PM #3 as their corresponding description.

TABLE-US-00003 TABLE 3 dDmjLUXR Major diameter of the ellipse in Poincar map derived from differences in time intervals TT and TP between i) R-peaks in cardiac signals and ii) crossover landmarks between the acquired red and infrared photo- photoplethysmographic signals (e.g., per example Poincar map analysis PM#3). dDmnLUXR Minor diameter of the ellipse in Poincar map derived from differences in time intervals TT and TP between i) R-peaks in cardiac signals and ii) crossover landmarks between the acquired red and infrared photo- photoplethysmographic signals (e.g., per example Poincar map analysis PM#3). dMeanLURP1 Mean of TP time interval (time interval between R-peak of the PSR/ECG X channel and the first crossover landmarks between the red and infrared photo-photoplethysmographic signals in the Poincar map analysis PM#3 landmarks). dMeanLURP2 Mean of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second crossover landmarks between the red and infrared photo-photoplethysmographic signals in the Poincar map example analysis PM#3 landmarks). dModeLURP1 Mode of TP time interval (time interval between R-peak of the PSR/ECG X channel and the first occurrence of the Poincar map analysis PM#3 landmarks) for the red photoplethysmographic signal. dModeLURP2 Mode of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second occurrence of the Poincar map analysis PM#3 landmarks for the red photoplethysmographic signal). dSkewLURP1 Skew of TP time interval (time interval between R-peak of the PSR/ECG X channel and the first occurrence of the Poincar map analysis PM#3 landmarks). dStdLURP2 Standard deviation of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second occurrence of the Poincar map analysis PM#3 landmarks). dRelMeanMedDiffLURP1 Ratio of mean -med/mean for two histograms: one for TP and one for TT, derived from Poincar map analysis PM#3 (e.g., per FIG. 7B).

[0204] Synchronicity Features Example #4

[0205] FIGS. 8A, 8B, and 8C illustrate another set of example dynamical properties of synchronicity between one or more acquired photoplethysmographic signals and one or more cardiac signals in accordance with an illustrative embodiment. In FIGS. 8A, 8B, and 8C, synchronicity is determined, via phase analysis, using landmarks in a cardiac signal.

[0206] FIG. 8A shows the phase 802 of a cardiac signal 104b as defined in one full revolution/period from one R-peak (e.g., 602) to the next R-peak (e.g., 602) and is shown superimposed over the underlying cardiac signal data set used to generate the phase data.

[0207] FIG. 8B shows the phases 804, 806 of the red and infrared photoplethysmographic signals as defined in one full revolution/period. The phase of the red photoplethysmographic signal and the infrared photoplethysmographic signal are shown from to (y-axis) using the Hilbert transform; the x-axis is time (index count of the data set). As shown in FIG. 8B, the two phases are red and infrared photoplethysmographic signals are coincident, signifying the two are synchronous.

[0208] FIG. 8C shows a phase difference between the cardiac signal and one of the photoplethysmographic signals as determined by a difference of the period of a photoplethysmographic signal and the period of a cardiac signal. In FIG. 8C the x-axis shows time (in index count of the data set) and the y-axis shows magnitude values of the calculated differences.

[0209] Indeed, synchronicity between acquired raw photoplethysmographic signals and cardiac signal based on phase differences between the cardiac signal and the photoplethysmographic signal(s) may be used to assess for presence, non-presence, severity, and/or localization (where applicable) of coronary artery disease, pulmonary hypertension, heart failure, among other disease, conditions, and associated conditions.

[0210] FIG. 8D shows phase difference analysis of a CAD-negative patient (i.e., a patient having a negative diagnosis of coronary artery disease) generated between an acquired infrared photoplethysmographic signal 304 and an acquired cardiac signal 104b. Specifically, FIG. 8D shows plot 808 of the periods 802 of a cardiac signal and periods 804 of the infrared photoplethysmographic signal. In plot 808, the x-axis is time (in index count of the data set) and the y-axis shows the phase (in radian). Plot 810 shows a calculated lag between periods 802 and 804. In some embodiments, the time/index lag is calculated via cross correlation between the two signals. The lag is the time interval the one signal needs to be shifted with respect to the other in order to yield the maximum cross correlation (or the minimum cross correlation). In plot 810, the x-axis is time (in index count of the data set) and the y-axis shows the cross-correlation value (unitless).

[0211] Plot 812 shows a frequency analysis of the difference data of plot 810. In plot 812, the x-axis is frequency (in Hz) and the y-axis is the relative amplitude of the signal. Plot 814 shows a difference between the infrared photoplethysmographic signal 304 and the cardiac signal 104b. In plot 814, the x-axis is time (in index count of the data set). Plot 816 shows a filtered version of the difference data of plot 814. Plot 818 shows a histogram of the filtered difference data of plot 816. In the histogram 818, the x-axis of the histogram shows difference amplitude (in bins derived from the difference data) and the y-axis shows the frequency/count.

[0212] FIG. 8E shows a phase difference analysis of a CAD-positive patient generated between an acquired infrared photoplethysmographic signal 304 and an acquired cardiac signal 104b. Specifically, FIG. 8E shows plot 820 of the periods 802 of a cardiac signal and periods 804 of the infrared photoplethysmographic signal. In plot 820, the x-axis is time (in index count of the data set) and the y-axis shows the phase (in radian). Plot 822 shows a calculated lag between periods 802 and 804. Plot 824 shows a frequency analysis of the difference data of plot 822. In plot 822, the x-axis is time (in index count of the data set) and the y-axis shows the cross-correlation value (unitless). In plot 824, the x-axis is frequency (in Hz) and the y-axis is the relative amplitude of the signal. Plot 826 shows a filtered difference between the infrared photoplethysmographic signal 304 and the cardiac signal 104b. In plot 826, the x-axis is time (in index count of the data set). Plot 828 shows a histogram of the filtered difference data of plot 824. In the histogram 828, the x-axis of the histogram shows difference amplitude (in bins derived from the difference data) and the y-axis shows the frequency/count.

[0213] Table 4 provides a list of example synchronicity feature extracted parameters associated with Phase analysis #4 as their corresponding description.

TABLE-US-00004 TABLE 4 dPhiDiffXL1Med Median value of the phase difference distribution belonging to part 1 distribution after the phase difference between a photoplethysmographic signal and cardiac signal are split into two parts: part 1 with higher mean mean. dPhiDiffXL2Med Median value of the phase difference distribution belonging to part 1 distribution after the phase difference between a photoplethysmographic signal and cardiac signal are split into two parts: here, part 2 with lower mean. dPhiDiffXL1Std Standard deviation of phase difference distribution belonging to Part 2 distribution after the phase difference between a photoplethysmographic signal and a cardiac signal are split into two parts: here, part 1 with higher mean. dPhiDiffXL2Std Standard deviation of phase difference distribution belonging to Part 2 distribution after the phase difference between a photoplethysmographic signal and a cardiac signal are split into two parts: part 1 with higher mean and part 2 with lower mean. dPhiDiffXLMean Mean value of the whole distribution for phase difference distribution between a photoplethysmographic signal and a cardiac signal. dPTT Pulse transit time: time difference (lag) between the phase of the PSR/ECG X channel and phase of the infrared photoplethysmographic signal.

[0214] Machine-Learning Based Classifier

[0215] Machine learning techniques predict outcomes based on sets of input data. For example, machine learning techniques are used to recognize patterns and images, supplement medical diagnoses, and so forth. Some machine learning techniques rely on a set of features generated using a training set of data (i.e., a data set of observations, in each of which an outcome to be predicted is known), each of which represents some measurable aspect of observed data, to generate and tune one or more predictive models. For example, observed signals (e.g., cardiac, plethysmographic, or other biophysical signals from a number of subjects, alone or in any number of combinations) may be analyzed to collect frequency, average values, and other statistical information about these signals. A machine learning technique may use these features to generate and tune a model that classifies or relates these features to one or more conditions, such as some form of cardiovascular disease or condition, including, e.g., coronary artery disease, heart failure, pulmonary hypertension, etc., and then apply that model to data, such biophysical data of one or more humans, to detect and/or to gain an understanding of the presence, non-presence, severity of one or more diseases or conditions (such as described herein) that might otherwise not be detectable or understandable to the same degree. Conventionally, in the context of cardiovascular disease, these features are manually selected from conventional electrocardiographic signals and combined by data scientists working with domain experts.

[0216] Examples of embodiments of machine learning include, but are not limited to, decision trees, random forests, SVMs, neural networks, linear models, Gaussian processes, nearest neighbor, SVMs and Nave Bayes. In some embodiments of the present disclosure, machine learning techniques may be implemented, e.g., as described in U.S. patent application Ser. No. 15/653,433, entitled Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions; and U.S. patent application Ser. No. 15/653,431, entitled Discovering Genomes to Use in Machine Learning Techniques; each of which is incorporated by reference herein in its entirety.

[0217] Experimental Results and Other Embodiments

[0218] FIG. 9 shows experimental results from a study that indicates clinical predictive value of certain dynamical features extracted from Poincar and phase analysis of one or more photoplethysmographic signal(s) (red photoplethysmographic signals and infrared photoplethysmographic signals) and one or more cardiac signals that indicates presence, non-presence, severity, and/or localization (where applicable) of a disease or abnormal condition, or an indication of one, in accordance with an illustrative embodiment.

[0219] In the study, candidate features were evaluated using t-test, mutual information, or AUC. T-tests were conducted against a null-hypothesis of normal LVEDP and null hypothesis of negative coronary artery disease. A t-test is a statistical test that can determine if there is a difference between two sample means from two populations with unknown variances. The output of the t-test is a dimensionless quantity known as a p-value. A small p-value (typically 0.05) indicates strong evidence against the null hypothesis. The study used random sampling with replacement (bootstrapping) to generate test sets.

[0220] Mutual information were conducted to assessed dependence of elevated or abnormal LVEDP or significant coronary artery disease on certain feature sets. Mutual information refers to an information theoretic measure of the mutual dependence between two random variables. MI is normalized by number of bins and the high and low MI are calculated

[00002]$\frac{\mathrm{normMI}}{\max \left(\mathrm{normMInoise}\right)}.$

[0221] as a high and a low of A selected feature has a high that is greater than 1.0 and a low that is greater than 1.0.

[0222] Table 1 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with Poincar map analysis PM #1. Table 2 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with Poincar map analysis PM #2. Table 3 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with Poincar map analysis PM #3.

[0223] Table 4 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with phase analysis example #4. The parameters may be configured as double precision variable.

[0224] Experimental Results for Features of Poincar Map Analysis #1

[0225] As discussed above, Table 1 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with Poincar map analysis PM #1. FIG. 9 shows the various geometric and statistical features extracted from a Poincar plot per Poincar map analysis PM #1 as described herein has potential clinical relevance in predicting and/or estimating presence, non-presence, severity, and/or localization (where applicable) of coronary artery disease and an elevated or abnormal LVEDP (which may indicate the presence, non-presence, and/or severity of a disease and/or condition).

[0226] Specifically, FIG. 9 (and reproduced in Tables 1-A and 1-B) shows that the major and minor diameters of a generated ellipse from Poincar map PM #1 for the PSR/ECG x channel (shown as dXDmj and dXDmn) has t-test p-value of 0.012 and 0.003, respectively, in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). Further, FIG. 9 shows that the minor diameter of a generated ellipse from Poincar map PM #1 for the PSR/ECG z channel (shown as dZDmn) has t-test p-value of 0.037 in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A small p-value (typically 0.05) indicates strong evidence against the null hypothesis (i.e., no presence of elevated or abnormal LVEDP; normal data set without elevated or abnormal LVEDP).

TABLE-US-00005 TABLE 1-A Feature Name Disease State Gender t-test p-value dXDmj LVEDP Female 0.012 Description: Major diameter of ellipse from Poincar map PM#1 for the PSR/ECG X channel

TABLE-US-00006 TABLE 1-B Feature Name Disease State Gender t-test p-value dXDmn LVEDP Female 0.003 dZDmn LVEDP Both Genders 0.037 Description: Minor diameter of ellipse from Poincar map PM#1 for the PSR/ECG X and Z channels

[0227] Further, FIG. 9 (and reproduced in Table 1-C) shows that the tilt angle, alpha, of ellipses from Poincar map analysis PM #1 on the PSR/ECG y and z channels (shown as dYAlpha and dZAlpha) has a t-test p-values of 0.049 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease; a t-test p-value of 0.039 in predicting estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A small p-value (typically 0.05) indicates strong evidence against the null hypothesis (i.e., no presence of an elevated or abnormal LVEDP).

TABLE-US-00007 TABLE 1-C Feature Name Disease State Gender t-test p-value dYAlpha CAD Both Genders 0.049 dZAlpha LVEDP Male 0.039 Description: Tilt angle, alpha, of the ellipse from Poincar map analysis PM#1 on the PSR/ECG Y and Z channels

[0228] In addition, FIG. 9 (and reproduced in Table 1-D) shows that amplitude mean of PSR/ECG x channel at the first intersection/crossover points of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dXMean1) have respective t-test p-value of 0.00064 and an AUC value of 0.548; a t-test p-value of 0.011 and an AUC value of 0.518, in predicting and/or estimating the presence, non-presence, localization, and/or severity of coronary artery disease in certain populations. A small p-value (typically 0.05) indicates strong evidence against the null hypothesis (i.e., no presence of an elevated or abnormal LVEDP); an AUC greater than 0.5 has significance in indicating the presence of CAD (which is defined as having greater than 70% stenosis by angiography or less than 0.80 fraction-flow by flow wire).

TABLE-US-00008 TABLE 1-D Feature Name Disease State Gender t-test p-value ROC-AUC dXMean1 CAD Female 0.00064 0.548 CAD Female 0.011 0.518 Description: Amplitude mean of PSR/ECG X channel at the first intersection/crossover points of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0229] In addition, FIG. 9 (and reproduced in Tables 1-E, 1-F, and 1-G) shows that the standard deviation of the distribution of the PSR/ECG x channel triggered by the first and the second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dXStd1 and dXStd2) has respective t-test p-value of 0.037 and 0.042 in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). Further, FIG. 9 shows that the standard deviation of the distribution of PSR/ECG y channel triggered by the second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dYStd2) has mutual information value of 1.143 in predicting/estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance; a p-value less than 0.05 has significance.

TABLE-US-00009 TABLE 1-E Feature Name Disease State Gender t-test p-value dXStd1 LVEDP Female 0.037 Description: Standard deviation of the distribution of the PSR/ECG X channel triggered by the first crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

TABLE-US-00010 TABLE 1-F Feature Name Disease State Gender t-test p-value dXStd2 LVEDP Both Genders 0.042 Description: Standard deviation of the distribution of the PSR/ECG X channel triggered by second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

TABLE-US-00011 TABLE 1-G Feature Name Disease State Gender Mutual Information dYStd2 CAD Male 1.143 Description: Standard deviation of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0230] Further, FIG. 9 (and reproduced in Table 1-H) shows that the kurtosis of the distribution of the PSR/ECG y channel triggered by the second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dYKurt2) has respective mutual information of 1.061 in p predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance.

TABLE-US-00012 TABLE 1-H Feature Name Disease State Gender Mutual Information dYKurt2 CAD Male 1.061 Description: Kurtosis of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0231] Further, FIG. 9 (and reproduced in Table 1-I) shows that the kurtosis of the distribution of the PSR/ECG z channel triggered by the second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dZKurt2) have mutual information value of 1.076 in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition); mutual information value of 1.192 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance.

TABLE-US-00013 TABLE 1-I Feature Name Disease State Gender Mutual Information dZKurt2 LVEDP Both Genders 1.076 CAD Female 1.192 Description: Kurtosis of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0232] Further, FIG. 9 (and reproduced in Table 1-J) shows that the mode of the distributions of the PSR/ECG y and z channels triggered by the second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dYMode2 and dZMode2) have mutual information values of 1.104 and 1.036, respectively, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance.

TABLE-US-00014 TABLE 1-J Feature Name Disease State Gender Mutual Information dYMode2 CAD Both Genders 1.104 dZMode2 CAD Male 1.036 Description: Kurtosis of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0233] Further, FIG. 9 (and reproduced in Tables 1-K and 1-L) shows that the mode of the distributions of the PSR/ECG z channel triggered by the first and second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dZSkew1 and dZSkew2) have mutual information values of 1.094 and 1.058, respectively, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance.

TABLE-US-00015 TABLE 1-K Feature Name Disease State Gender Mutual Information dZSkew1 CAD Female 1.094 Description: Kurtosis of distribution of the PSR/ECG Z channel data triggered at first crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

TABLE-US-00016 TABLE 1-L Feature Name Disease State Gender Mutual Information dZSkew2 CAD Both Gender 1.058 Description: Kurtosis of distribution of the PSR/ECG Z channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0234] In addition, FIG. 9 (and reproduced in Table 1-M) shows that the relative difference between the standard deviation and median absolute deviation (MAD) of distribution of the PSR/ECG y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dYRelStdMAD2) has a t-test p-values of 0.042 and a mutual information value of 1.048 in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition).

TABLE-US-00017 TABLE 1-M Feature Name Disease State Gender t-test p-value Mutual Information dYRelStdMAD2 CAD Male 0.042 1.048 Description: Relative difference between the standard deviation and median absolute deviation (MAD) of distribution of the PSR/ECG Y channel data triggered at second crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0235] Further, FIG. 9 (and reproduced in Table 1-N) shows that the relative difference between the standard deviation and median absolute deviation (MAD) of distribution of the PSR/ECG z channel data triggered at first crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM #1) (shown as dZRelStdMAD1) has a t-test p-values of 0.041 in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A p-value less than 0.05 has significance; a mutual information value greater than 1.0 has significance.

TABLE-US-00018 TABLE 1-N Feature Disease t-test Name State Gender p-value dZRelStdMAD1 LVEDP Female 0.041 Description: Relative difference between the standard deviation and median absolute deviation (MAD) of distribution of the PSR/ECG Z channel data triggered at first crossover landmarks of photoplethysmographic signals (e.g., in Poincar map analysis PM#1)

[0236] Experimental Results for Features of Poincar Map Analysis #2

[0237] As discussed above, Table 2 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with Poincar map analysis PM #2. FIG. 9 shows the various geometric and statistical features extracted from a Poincar plot per Poincar map analysis PM #2 as described herein has potential clinical relevance in predicting the presence of coronary artery disease and an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition).

[0238] Specifically, FIG. 9 (and reproduced in Tables 2-A and 2-B) shows that the major diameters of a generated ellipses in Poincar maps derived from amplitude of infrared and red photo-photoplethysmographic signals at R-peaks of a cardiac signal (shown as dDmjL and dDmjU) has t-test p-value of 0.031 and 0.007, respectively, in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). Further, FIG. 9 shows that the major diameter of a generated ellipses in Poincar maps derived from amplitude of infrared photo-photoplethysmographic signal at R-peaks of a cardiac signal (shown as dDmjL) has a t-test p-value of 0.035; mutual information value of 1.104; AUC of 0.502 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease.

TABLE-US-00019 TABLE 2-A Feature Disease t-test Mutual ROC- Name State Gender p-value Information AUC dDmjL CAD Female 0.035 1.104 0.502 LVEDP Both Genders 0.031 n/s n/s Description: Major diameter of the ellipse in Poincar map derived from amplitude of infrared photo-photoplethysmographic signal at R-peaks of a cardiac signal (e.g., per example Poincar map analysis PM#2)

TABLE-US-00020 TABLE 2-B Feature Disease t-test Name State Gender p-value dDmjU LVEDP Both Genders 0.007 Description: Major diameter of the ellipse in Poincar map derived from amplitude of red photo- photoplethysmographic signal at R-peaks of a cardiac signal (e.g., per example Poincar map analysis PM#2)

[0239] Further, FIG. 9 (and reproduced in Table 2-C) shows that the minor diameters of a generated ellipses in Poincar maps derived from amplitude of the red photo-photoplethysmographic signal at R-peaks of a cardiac signal (shown as dDmnU) has t-test p-value of 0.0380, respectively, in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A mutual information of greater than 1.0 has significance; an AUC value greater than 1.0 has significance; p-value less than 0.05 has significance.

TABLE-US-00021 TABLE 2-C Feature Disease t-test Name State Gender p-value dDmnU LVEDP Both Genders 0.038 Description: Minor diameter of the ellipse in Poincar map derived from amplitude of red photo- photoplethysmographic signal at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM#2)

[0240] In addition, FIG. 9 (and reproduced in Tables 2-D and 2-E) shows that the tilt angles, alpha, of the ellipses in Poincar maps derived from amplitude of the infrared and red photo-photoplethysmographic signals at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM #2) (shown as dAlphaL and dAlphaU) have respective mutual information values of 1.043 and 1.03 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance.

TABLE-US-00022 TABLE 2-D Feature Disease Mutual Name State Gender Information dAlphaL CAD Female 1.043 Description: Tilt angle, alpha, of the ellipse in Poincar map derived from amplitude of the infrared photo- photoplethysmographic signal at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM#2)

TABLE-US-00023 TABLE 2-E Feature Disease Mutual Name State Gender Information dAlphaU CAD Both Genders 1.03 Description: Tilt angle, alpha, of the ellipse in Poincar map derived from of the amplitude red photo-photoplethysmographic signal at R-peak of a cardiac signal (e.g., per example Poincar map analysis PM#2)

[0241] In addition, FIG. 9 (and reproduced in Table 2-F) shows that the kurtosis of the histogram of the infrared photoplethysmographic signal at R-peaks of a cardiac signal (shown as dKurtL) has a mutual information value of 1.171 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A mutual information value of greater than 1.0 has significance.

TABLE-US-00024 TABLE 2-F Feature Disease Mutual Name State Gender Information dKurtL LVEDP Both Genders 1.171 Description: Kurtosis of histogram of Poincar map analysis PM2 of infrared photoplethysmographic signal

[0242] In addition, FIG. 9 (and reproduced in Tables 2-G and 2-H) shows that the mean of the histogram of the infrared and red photoplethysmographic signals at R-peaks of a cardiac signal (shown as dMeanL and dMeanU) have respective t-test p-values of 0.033 and 0.003, respectively, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). Further, FIG. 9 shows that the mean of the histogram of the infrared and red photoplethysmographic signals at R-peaks of a cardiac signal (shown as dMeanL and dMeanU) have respective mutual information value of 1.012 and an AUC value of 0.516; mutual information value 1.091, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A mutual information of greater than 1.0 has significance; an AUC value greater than 1.0 has significance.

TABLE-US-00025 TABLE 2-G Feature Disease t-test Mutual ROC- Name State Gender p-value Information AUC dMeanL CAD Female n/s 1.012 0.516 LVEDP Male 0.033 n/s n/s Description: Mean value of histogram of Poincar map analysis PM2 of the infrared photoplethysmographic signals

TABLE-US-00026 TABLE 2-H Feature Disease t-test Mutual Name State Gender p-value Information dMeanU CAD Female n/s 1.091 LVEDP Both Genders 0.003 n/s Description: Mean value of histogram of Poincar map analysis PM#2 for red photoplethysmographic signal

[0243] In addition, FIG. 9 (and reproduced in Tables 2-I and 2-J) shows that the mode of the histogram of the infrared and red photoplethysmographic signals at R-peaks of a cardiac signal (shown as dModeLP and dModeUP) have respective t-test p-values of 0.024 and 0.004 in predicting and/or estimating the presence, non-presence, and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). Further, FIG. 9 shows that the mode of the histogram of the infrared photoplethysmographic signal at R-peaks of a cardiac signal (shown as dModeLP) has an AUC value of 0.507 in predicting the presence of coronary artery disease. An AUC value greater than 1.0 has significance; a p-value value less than 0.05 has significance.

TABLE-US-00027 TABLE 2-I Feature Disease t-test ROC- Name State Gender p-value AUC dModeLP CAD Both Genders n/s 0.507 LVEDP Both Genders 0.024 n/s Description: Mode of the distribution (histogram) of Poincar map analysis PM#2 for the infrared photoplethysmographic signal

TABLE-US-00028 TABLE 2-J Feature Disease t-test Name State Gender p-value dModeUP LVEDP Both Genders 0.004 Description: Mode of the distribution (histogram) of Poincar map analysis PM#2 for the infrared photoplethysmographic signal

[0244] In addition, FIG. 9 (and reproduced in Table 2-K) shows that the standard deviation of the histogram of the red photoplethysmographic signal at R-peaks of a cardiac signal (shown as dStdU) has an AUC value of 0.511 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. An AUC value greater than 1.0 has significance.

TABLE-US-00029 TABLE 2-K Feature Disease ROC- Name State Gender AUC dStdU CAD Female 0.511 Description: Standard deviation of histogram of Poincar map analysis PM#2 for the red photoplethysmographic signal

[0245] Experimental Results for Features of Poincar Map Analysis #3

[0246] As discussed above, Table 3 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with Poincar map analysis PM #3. FIG. 9 shows the various geometric and statistical features extracted from a Poincar plot per Poincar map analysis PM #3 as described herein has potential clinical relevance in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease and an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition).

[0247] Specifically, FIG. 9 (and reproduced in Tables 3-A and 3-B) shows that the major diameters of a generated ellipses in Poincar maps derived from differences in time intervals TT and TP between i) R-peaks in cardiac signals and ii) crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals (shown as dDmjLUXR) has an AUC value of 0.501, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. Further, FIG. 9 shows that the minor diameters of a generated ellipses in Poincar maps derived from differences in time intervals TT and TP between i) R-peaks in cardiac signals and ii) crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals (shown as dDmLUXR) has a t-test p-value of 0.02, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A p-value less than 0.05 has significance; an AUC value greater than 0.5 has significance.

TABLE-US-00030 TABLE 3-A Feature Disease ROC- Name State Gender AUC dDmjLUXR CAD Both Genders 0.501 Description: Major diameter of the ellipse in Poincar map derived from differences in time intervals TT and TP between i) R-peaks in cardiac signals and ii) crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals (e.g., per example Poincar map analysis PM#3)

TABLE-US-00031 TABLE 3-B Feature Disease t-test Name State Gender p-value dDmnLUXR LVEDP Female 0.02 Description: Minor diameter of the ellipse in Poincar map derived from differences in time intervals TT and TP between i) R- peaks in cardiac signals and ii) crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals (e.g., per example Poincar map analysis PM#3)

[0248] In addition, FIG. 9 (and reproduced in Tables 3-C and 3-D) shows that the means of TP and TT time intervals (i.e., time interval between R-peak of the PSR/ECG x channel and the respective first and second crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals) (shown as dMeanLURP1 and dMeanLURP2) have a t-test p-value of 0.013 and 0.02, respectively, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition).

TABLE-US-00032 TABLE 3-C Feature Disease t-test Name State Gender p-value dMeanLURP1 LVEDP Both Genders 0.013 Description: Mean of TP time interval (time interval between R-peak of the PSR/ECG X channel and the first crossover landmarks between the red and infrared photo- photoplethysmographic signals in the Poincar map analysis PM#3 landmarks)

TABLE-US-00033 TABLE 3-D Feature Disease t-test Name State Gender p-value dMeanLURP2 LVEDP Male 0.02 Description: Mean of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second crossover landmarks between the red and infrared photo- photoplethysmorgraphic signals in the Poincar map example analysis PM#3 landmarks)

[0249] In addition, FIG. 9 (and reproduced in Tables 3-E and 3-F) shows that the modes of TP and TT time intervals (i.e., time interval between R-peak of the PSR/ECG x channel and the respective first and second crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals) (shown as dModeLURP1 and dModeLURP2) have a t-test p-value of 0.013 and 0.028, respectively, in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition).

TABLE-US-00034 TABLE 3-E Feature Disease t-test Name State Gender p-value dModeLURP1 LVEDP Both Genders 0.013 Description: Mode of TP time interval (time interval between R-peak of the PSR/ECG X channel and the first occurrence of the Poincar map analysis landmarks) for the red PM#3 photoplethysmographic signal

TABLE-US-00035 TABLE 3-F Feature Disease t-test Name State Gender p-value dModeLURP2 LVEDP Male 0.028 Description: Mode of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second occurrence of the Poincar map analysis landmarks for the PM#3 red photoplethymographic signal

[0250] In addition, FIG. 9 (and reproduced in Table 3-G) shows that the skew of TP time interval (i.e., time interval between R-peak of the PSR/ECG x channel and the first crossover landmarks between the acquired red and infrared photo-photoplethysmographic signals) (shown as dSkewLURP1 has a t-test p-value of 0.034 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of presence of coronary artery disease.

TABLE-US-00036 TABLE 3-G Feature Disease t-test Name State Gender p-value dSkewLURP1 CAD Both Genders 0.034 Description: Skew of TP time interval (time interval between R-peak of the PSR/ECG X channel and the first occurrence of the Poincar map analysis PM#3 landmarks)

[0251] In addition, FIG. 9 (and reproduced in Table 3-H) shows that the standard deviation of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second occurrence of the Poincar map analysis PM #3 landmarks) (shown as dStdLURP2) has a mutual information value of 1.486 and an AUC value of 0.541 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of presence of coronary artery disease.

TABLE-US-00037 TABLE 3-H Feature Disease Mutual ROC- Name State Gender Information AUC dStdLURP2 CAD Both Genders 1.486 0.541 Description: standard deviation of TT time interval (time interval between R-peak of the PSR/ECG X channel and the second occurrence of the Poincar map analysis PM#3 landmarks)

[0252] In addition, FIG. 9 (and reproduced in Table 3-I) shows that the standard deviation of TT time interval (time interval between R-peak of the PSR/ECG x channel and the second crossover landmark between the acquired red and infrared photo-photoplethysmographic signals) (shown as dRelMeanMedDiffLURP1) has an AUC value of 0.5 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of the presence of coronary artery disease.

TABLE-US-00038 TABLE 3-I Feature Disease ROC- Name State Gender AUC dRelMeanMedDiffLURP1 CAD Both Genders 0.5 Description: Ratio of mean -med/mean for two histograms: one for TP and one for TT, derived from Poincar map analysis PM#3 (e.g., per FIG. 7B).

[0253] Experimental Results for Features of Phase Analysis #4

[0254] As discussed above, Table 4 provides a description of each of the assessed synchronicity extracted parameters of FIG. 9 associated with phase analysis example #4. FIG. 9 also shows the various geometric and statistical features extracted from phase analysis #4 as described herein has potential clinical relevance in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease and an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition).

[0255] Specifically, FIG. 9 (and reproduced in Table 4-A) shows that the median values of the phase difference distribution belonging to the first distribution after the phase difference between a photoplethysmographic signal and cardiac signal are split into two parts: part 1 with higher mean and part 2 with lower mean (shown as dPhiDiffXL1Med) has a t-test p-value of 0.015 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A p-value less than 0.05 has significance.

TABLE-US-00039 TABLE 4-A Feature Disease t-test Name State Gender p-value dPhiDiffXL1Med CAD Both Genders 0.015 Description: Median value of the phase difference distribution belonging to part 1 distribution after the phase difference between a photoplethysmographic signal and cardiac signal are split into two parts: part 1 with higher mean.

[0256] In addition, FIG. 9 (and reproduced in Table 4-B) shows that the standard deviation of the phase difference distribution belonging to the second distribution after the phase difference between a photoplethysmographic signal and cardiac signal are split into two parts: part 1 with higher mean and part 2 with lower mean (shown as dPhiDiffXL2Std), has an AUC value of 0.502 of predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. An AUC greater than 0.5 has significance.

TABLE-US-00040 TABLE 4-B Feature Disease ROC- Name State Gender AUC dPhiDiffXL2Std CAD Male 0.502 Description: Standard deviation of phase difference distribution belonging to Part 2 distribution after the phase difference between a photoplethysmographic signal and a cardiac signal are split into two parts: here, part 2 with lower mean.

[0257] In addition, FIG. 9 (and reproduced in Table 4-C) shows that the mean of the phase difference distribution between a photoplethysmographic signal and cardiac signal (shown as dPhiDiffXLMean) has a t-test p-value of 0.26 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of coronary artery disease. A p-value less than 0.05 has significance.

TABLE-US-00041 TABLE 4-C Feature Disease t-test Name State Gender p-value dPhiDiffXLMean CAD Both Genders 0.026 Description: Mean value of the whole distribution for phase difference distribution between a photoplethysmographic signal and a cardiac signal

[0258] In addition, FIG. 9 (and reproduced in Table 4-D) shows that the pulse transit time (i.e., time difference (lag) between the phase of the PSR/ECG x channel and phase of the infrared photoplethysmographic signal) (shown as dPTT) has a t-test p-value of 0.045 in predicting and/or estimating the presence, non-presence, localization (where applicable), and/or severity of an elevated or abnormal LVED (which may indicate the presence, non-presence, and/or severity of a disease or condition). A p-value less than 0.05 has significance.

TABLE-US-00042 TABLE 4-D Feature Disease t-test Name State Gender p-value dPTT LVEDP Female 0.045 Description: Pulse transit time: time difference (lag) between the phase of the PSR/ECG X channel and phase of the infrared photoplethysmographic signal

[0259] Coronary Artery DiseaseLearning Algorithm Development Study

[0260] A Coronary Artery DiseaseLearning Algorithm Development (CADLAD) study was untaken that acquired photoplethysmographic signals and cardiac signals to support the development and testing of the machine-learned algorithms.

[0261] In the study, paired clinical data were used to guide the design and development of the pre-processing, feature extraction, and machine learning phase of the development. That is, the collected clinical study data are split into cohorts: a training cohort, a validation cohort, and a verification cohort. In the study, each acquired data set is first pre-processed to clean and normalize the data. Following the pre-processing processes, a set of features are extracted from the signals in which each set of features is paired with a representation of the true conditionfor example, the binary classification of the presence or absence of significant CAD or the scored classification of the presence of significant CAD in a given coronary artery.

[0262] The assessment system (e.g., 114, 114a, 114b), in some embodiments, automatically and iteratively explores combinations of features in various functional permutations with the aim of finding those combinations which can successfully match a prediction based on the features. To avoid overfitting of the solutions to the training data, the validation set is used as a comparator. Once candidate predictors have been developed, they are then manually applied to a verification data set to assess the predictor performance against data that has not been used at all to generate the predictor. Provided that the data sets are sufficiently large, the performance of a selected predictor against the verification set will be close to the performance of that predictor against new data.

[0263] The study also developed and evaluated machine learning-based predictive models that employ nonlinear dynamics and chaos for extracting physically meaningful and significant features from the cardiac biopotential and photoplethysmographic signal data. Traditional features based on linear characterizations of the signals are not capable of detecting more complex and nonlinear patterns hidden in the signals. In the study, by employing nonlinear dynamics, three categories of features were developed: (i) features based on the dynamics of the cardiac system represented by biopotential signal, (ii) features based on the dynamics represented by the PPG signals and (iii) features characterizing the synchronicity between the two dynamics.

[0264] For the first two sets, invariant measures of the dynamics such as Lyapunov exponent (LE), fractal dimension (D2) and rate of entropy (K2) were computed. Lyapunov exponent is a global measure that characterizes the strength of the exponential divergence [30]. For chaotic systems, the maximum Lyapunov exponent is a positive number which indicates that the system has less memory of the past. For a given dynamical system, as Lyapunov exponent value becomes larger, the time horizon over which the past information can be used to predict the future becomes shorter. Entropy (KS) (or Kolmogorov Sinai entropy K2 [31, 32]) represents the rate of change of entropy with time. Fractal dimension (D2) characterizes the topological property of an attractor in phase space and can be used to reveal more about the dynamics in combining the geometric information of the attractor (fractality) and how the dynamics evolve on it [33]. An example of an attractor of the acquired cardiac and photoplethysmographic signals are shown in FIGS. 4A and 4B.

[0265] Nonlinear dynamics and chaos theory systematically can be used to explain the complexity of linear system systems and provides tools to quantitatively analyze their behavior [19]. Linear systems can generate responses which grow/decay exponentially or oscillate periodically or a combination thereof in which any irregular pattern in the response may be ascribed to irregularity or randomness in the inputs to these systems. Linear systems are a simplification of reality, and most dynamical systems whether natural or man-made are inherently nonlinear which can produce complex irregular behavior even without any source of randomness. These behaviors are often called deterministic chaos. Nonlinear dynamics and chaos tools have been used to explain various complex biological and physiological phenomena [20, 21, 22, 23]; for example, to classify atrial fibrillations [24] and to characterize heart rate variability [25], each of where is incorporated by reference here in its entirety. Further description of these dynamical features are described in U.S. Provisional Patent Application No. 62/862,991, filed Jun. 18, 2019, entitled Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals, which is incorporated by reference herein in its entirety.

[0266] Other invariant measure of dynamics may be used as a feature set. Deterministic dynamical systems that exhibit chaotic behavior often possess invariant properties which do not depend on when the observations are made and are thus independent of the evolution of the system.

[0267] For the synchronicity feature sets, three types of Poincar maps were defined and the resulting sets were characterized statistically and geometrically. The computed features set matched with appropriate labels were then used to train several machine learning models. Model were selected based on its respective AUC performance on a holdout test set. The study performed cross validation and grid searches to tune the hyperparameters used in the classifier training. In the study, a developed Elastic Net model was observed to have AUCs of 0.78 and 0.61 in CAD classification on two tested data sets. And a developed XGboost model was observed to have AUCs of 0.86 and 0.63 on two tested data sets. The study demonstrated an efficient and cost-effective means of using advanced nonlinear feature extraction processes of non-invasive modalities for machine learning operations for disease or abnormal condition prediction.

[0268] Elastic Net, Lasso, or Ridge classifiers are generally suited for smaller datasets with a large number of features because they can be adjusted to prevent overfitting. Elastic Net is a hybrid of Lasso and Ridge, where both the absolute value penalization (Lasso) and squared penalization (Ridge) are included. For each penalty, hyperparameters exist that can be optimized to generate stronger models. Lasso and Ridge only have a single hyperparameter each which makes optimization more limited.

[0269] Data description. In the study, two human subject cohorts with an average age of 63 (group A) and 28 (group B) were recruited for data collection. Subjects of the cohorts were selected after undergoing a qualification screening process. For the older group, the CAD labels and LVEDP values were determined by the corresponding gold standard tests while the younger group was considered to be healthy by clinical criteria. That is, the younger group did not have CAD and their LVEDP values were not abnormally high or elevated.

[0270] From each subject in groups A and B, cardiac signals (as biopotential signals) and photoplethysmographic signals as time series data were acquired. Data of both signal modalities were acquired over 3.5 minutes, and the entire procedure took about 10 minutes per subject on average. The cardiac signals were each collected at a sampling rate of 8 KHz (i.e., 8,000 samples per seconds for each of 6 channels collected over 210 seconds) using a phase space recorder as described in relation to FIGS. 3A-3E. Three differential input pairs were arranged orthogonally at a subject's thorax along with a reference lead. The acquired signals were used for feature extraction after removing baseline wander and filtering out powerline and high frequency noise.

[0271] During the same procedure in which the cardiac signals were collected from a subject, photoplethysmographic signals were collected at a sampling rate of 500 Hz using the same phase space recorder. Photo-absorption data of red and infrared channels were each recorded at 500 samples per second over the same 210 second period. These photoplethysmographic and cardiac signals were simultaneously acquired for each subject. Jitter (inter-modality jitter) in the data was less than about 10 microseconds (s). Jitter among the cardiac signal channels were around 10 femtoseconds (fs).

[0272] CAD Feature Study. The study used a definition for significant coronary occlusions as either having greater than 70% stenosis or a patient that passed a functional threshold for blood flow limitation [14, 15]. For group A, two-vessel disease (i.e., two vessels with lesions meeting this definition) was considered as being disease-positive, and non-disease cases were defined as healthy control subjects that had underwent invasive catheterization for evaluation of coronary artery disease but did not have any coronary lesions. Table 5 lists the number of positive and negative cases in the coronary artery disease data set used in the development of coronary artery disease features for the study. Table 5 further shows the average age and gender composition associated with the subjects in the data set. The study used invasive coronary angiography, the gold standard for coronary artery disease, as the ground truth metric. In coronary angiography, fluoroscopy is used to image coronary arteries following an injection of a radiopaque contrast agent. With coronary angiography, stenoses (blockages) in the arteries may be detected and patients are subsequently labelled as CAD-positive or CAD-negative.

TABLE-US-00043 TABLE 5 Group Total Positive Negative Male Female Age IQR A 1211 463 748 61% 39% 56-71 B 358 0 358 42% 58% 21-31 Total 1569 463 1106 57% 43%

[0273] The study results show that synchronicity between photoplethysmographic signals and cardiac signals, represented by way of synchronicity features from analysis between photoplethysmographic signals and cardiac signals as described herein, can be used to predict the presence or non-presence of significant coronary artery disease.

[0274] LVDEP Feature Feasibility Study. Left ventricular end diastolic pressure (LVEDP) is an invasively-obtained hemodynamic measurement used to describe the heart's left-sided filling pressures in patients undergoing cardiac catheterization. LVEDP is a critical parameter in the hemodynamic evaluation of patients with either systolic or diastolic LV dysfunction, which are both associated with decreased LV compliance. Alterations in the pressure-volume relationships that result in markedly elevated filling pressures are the hallmark of cardiomyopathies [10].

[0275] Measurement of filling pressures may be used to assess risk stratification and the development of an appropriate treatment strategy. Furthermore, LVEDP provides important prognostic information, as elevated LVEDP has been established as an independent predictor of adverse outcomes in the setting of acute myocardial infarct [16], cardiogenic shock [17], post-procedural success of cardiac surgery [18], and percutaneous cardiac interventions. Table 6 lists the number of positive and negative cases of LVEDP used in the evaluation of LVEDP features in the data set of Table 5.

TABLE-US-00044 TABLE 6 Group Total High Low Male Female Age IQR A 470 211 259 60% 40% 57-71 B 418 0 418 42% 58% 21-35 Total 888 211 677 52% .sup.48% -

[0276] The study results show that the synchronicity between photoplethysmographic signals and cardiac signals, represented by way of synchronicity features from analysis between photoplethysmographic signals and cardiac signals as described herein, can be used to predict presence or non-presence of abnormal LVEDP.

[0277] Machine-Learning Classifier Analysis. In the study, feature sets extracted from the acquired data set, including 94 synchronicity features defined between the photoplethysmographic signals and cardiac signals (e.g., per synchronicity analysis of Poincar maps 1, 2 and 3), as well as 6 features of the phase analysis #4, among others (e.g., dynamical features, etc.), were extracted and assessed in a machine-based classifier analysis. The feature sets including the synchronicity features were paired with the corresponding CAD or LVEDP labels and provided as input to the machine learning models. The feature sets included 36 other dynamical features associated with cardiac signals (i.e., biopotential signals) and 29 further dynamical features associated with photoplethysmographic signals were also evaluated. These features are described in U.S. Patent application No. ______, entitled Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals, concurrently filed with the instant application (and claimed priority to U.S. Provisional Patent Application No. 62/863,005, filed Jun. 18, 2019), which is incorporated by reference herein in its entirety.

[0278] In the classifier analysis, the data for CAD and LVEDP were each split into a training-validation set and a test set. Table 7 shows the composition of the training-validation and test data sets for the machine learning model training and evaluation. As noted above, information about Groups A and B for the CAD data sets are listed in Table 5 and information about Groups A and B for the LVEDP data sets are listed in Table 6.

TABLE-US-00045 TABLE 7 ML Data set Composition Train-validation 80% A + 50% B Test 1 20% A + 50% B Test 2 20% A

[0279] The training-validation set is used to train and fine-tune candidate machine learning models using 5-fold cross validation. Table 8 lists the classifiers used in the study for training and model selection for both the CAD and LVEDP data sets. The pipeline for data scaling, model training, grid search and model evaluation was implemented in Python using the Scikit-learn package [36].

TABLE-US-00046 TABLE 8 1 Gradient tree boosting (XGBClassifier) [37] 2 K nearest neighbors classifier (KNeighborsClassifier) 3 support vector classifier (SVC) 4 Random forest classifier 5 Logistic regression 6 Elastic net (ElasticNet) [38]

[0280] To find an optimal set of hyperparameters for each model, the study performed a grid search over a pre-defined range of hyperparameters. Using average AUC as the performance metric, the best hyper-parameters set is selected for each model. The selected models are then trained on the entire training-validation set and their AUC performance on the holdout test sets is ranked.

[0281] In the study, the Elastic Net model and the support vector classifier model were found to be most predictive for significant CAD predictions, and the XGBoost model and Elastic Net model were found to be most predictive for an elevated or abnormal LVEDP state. Table 9 shows the predictive performance of the Elastic Net model and the support vector classifier model to predict significant CAD. Table 10 shows the predictive performance of the Elastic Net model and the XGBoost model to predict a significant CAD state.

TABLE-US-00047 TABLE 9 AUC Rank Model Training Test 1 Test 2 1 Elastic net 0.71 0.78 0.61 2 Linear SVC 0.75 0.65 0.52

TABLE-US-00048 TABLE 10 AUC Rank Model Training Test 1 Test 2 1 XGBoost 1.0 0.86 0.63 2 ElasticNet 0.79 0.84 0.51

[0282] FIGS. 10A and 10B, respectively, show ROC curves of the significant CAD classification using the trained Elastic Net model on Test 1 and Test 2. FIGS. 10C and 10D, respectively, show ROC curves of the abnormal LVEDP classification using the trained XGBoost model on Test 1 and Test 2.

[0283] As shown in FIGS. 10A, 10B, 10C, and 10D, CAD and LVEDP classification tasks can achieve relatively good AUC performance. In the case of CAD predictions, AUC=0.78 on Test 1 and AUC=0.61 on Test 2 were observed. For elevated or abnormal LVEDP predictions, AUC=0.86 on Test 1 and AUC=0.63 on Test 2 were observed.

[0284] The models were trained on both Groups A and B (older and younger subjects, respectively) data sets, as described in relation to Tables 5 and 6. The use of the Group B data set augments the training of the Group A data set and allows the models to learn very healthy from diseased subjects. As a result, the model honed for this task exhibits better performance on Test 1 (which contains subjects from both Groups A and B) as compared to Test 2 (which is from Group A only). Further, because the acquired data sets are skewed toward non-diseased cases, as shown in Tables 5 and 6, consequently, the trained model better in this study at detecting CAD-negative subjects. It is expected that with data sets that are more balanced between diseased and non-diseased cases, model performance for Test 2 would be improved. XGBoost performance may also be improved by performing a more refined hyperparameter search and stronger regularization.

[0285] Further improvements to a second Elastic Net model was made using only the synchronicity feature sets and with a larger data set.

[0286] FIG. 10E shows a ROC curve for the correct classification of presence of significant CAD using a subsequently trained Elastic Net model using only the synchronicity feature sets in accordance with an illustrative embodiment.

[0287] FIG. 10F shows a ROC curve of the correct classification of presence of elevated or abnormal LVEDP using a subsequently trained Elastic Net model using only the synchronicity feature sets in accordance with an illustrative embodiment.

[0288] FIGS. 10E and 10F shows that synchronicity feature sets, in combination with the features, can be used to achieve classification with high specificity and sensitivity.

[0289] FIGS. 10G and 10H, respectively, shows the feature contribution in the classifier models of FIGS. 10E and 10F for CAD and LVEDP classifications. In FIG. 10G, features are divided into three sub-groups (PM1, PM2, PM3) based on the Poincar Map (PM) used to generate them. In FIG. 10H, absolute values of difference in feature contributions used in LVEDP and CAD classification models are shown. Features with larger differences indicates the feature to be a more disease specific feature. Table 1 includes some of the features of PM1 as shown in FIGS. 10G and 10H. Table 2 includes some of the features of PM2 as shown in FIGS. 10G and 10H. Table 3 includes some of the features of PM3 as shown in FIGS. 10G and 10H.

[0290] Table 11 lists the accumulative feature contributions in each of the sub-groups PM1-PM3 as denoted in FIGS. 10G and 10H. As shown in Table 11, PM3 features have the highest contribution in the classification of both CAD and LVEDP, while PM1 has the lowest contribution. In Table 11, the sum of feature contribution of Poincar maps used in the Elastic Net model for the classification of CAD and LVEDP are shown.

TABLE-US-00049 TABLE 11 Poincar Poincar Poincar Target Map 1 Map 2 Map 3 CAD 0.116 0.207 0.677 LVEDP 0.214 0.325 0.461

[0291] Although Elastic Net classifiers were found to be the best performing model to classify CAD and LVEDP, synchronization features contribute differently across the two diseases. The absolute value of the difference in the feature contribution is plotted in FIG. 11. This reflects the utility of the synchronization features that have different distribution among LVEDP and CAD subjects. These PMs were developed based on triggers and other information that was thought to be useful from a signal perspective; the current work describes their utility in disease assessment, but the underlying physiological characteristics captured by these features are unknown, and future work will explore that mechanistic aspect.

[0292] LVDEP Feature Performance Study. A second LVDEP-related study was conducted to predict, as a primary outcome, an elevated LVEDP. This study also investigated as secondary outcomes (i) the diagnostic sensitivity of the machine-learned predictor among three sub-groups of increasing LVEDP (20 mmHg, 25 mmHg, and 30 mmHg) and (ii) the predictive performance of the predictor within an age and gender propensity matched cohort.

[0293] The second LVDEP-related study used data sets collected in the manner described herein (i.e., using aphase space recorder as described in relation to FIGS. 3A-3E) from a cardiac phase space analytic study to retrospectively develop and evaluate machine-learned predictors. Biopotential (cardiac) signals and photoplethysmographic signals were acquired from 1,919 consecutive subjects enrolled across 21 centers immediately prior to elective angiography. A comparison (control arm) of 634 healthy subjects without cardiovascular disease enrolled across 2 of the 21 sites underwent data collected in the identical manner as described for the 1,919 subjects.

[0294] Data of both signal modalities were acquired over 3.5 minutes and the entire procedure took about 10 minutes. The biopotential signals were collected with a sampling rate of 8 KHz (i.e., 8,000 samples per seconds for each of 6 channels over 210 seconds). Three differential input pairs were arranged orthogonally at the patient's thorax along with a reference lead. The acquired signals were used for feature extraction after removing baseline wander and filtering powerline and high frequency noise.

[0295] Out of the 1,919 symptomatic subjects who underwent elective angiography, 256 subjects were found on catheterization to have an LVEDP20 mmHg; these 256 subjects formed the study cohort. As noted, the patients were referred to angiography for the evaluation of symptoms, and elevated or abnormal LVEDP was determined for each patient, when present, during cardiac catheterization with direct LV pressure measurements during ventriculography.

[0296] To develop the machine learned predictors, cross-validation was performed over 100 iterations, with 70% of the subjects used for training and 30% for testing. The subjects were divided to stratify prevalence of disease (LVEDP20 mmHg) across the sets, but division was otherwise random. The training subjects' features were inputted to an Elastic Net model configured with added regularization penalties to reduce overfitting. Once trained, the model was applied to the validation subjects to assess diagnostic performance.

[0297] FIGS. 11A-11F show experimental results for a trained classifier to predict an elevated LVEDP in accordance with an illustrative embodiment. FIG. 11A shows show a ROC curve of classification to predict an elevated LVEDP20 mmHg. The classification is based on an Elastic Net model. As shown in FIG. 11A, a machine-learned cardiac phase space predictor provides a robust prediction of elevated LVEDP20 with an AUC of 0.97. The predictor algorithm also performs with an increasing diagnostic sensitivity across progressive increases in LVEDP. The algorithm maintained a high level of fidelity even after age and gender propensity matching with an area under the curve for prediction of LVEDP20 of 0.88. The ROC curve were computed with R package ROC and includes AUC, Sensitivity, specificity, PPV and NPV values.

[0298] FIG. 11B shows the ROC curve illustrating diagnostic performance of the machine-learned approach in a propensity matched secondary analysis (age and gender) to predict an LVEDP20 mmHg. In the analysis, subjects with elevated LVEDP were propensity matched to those with non-elevated LVEDP based on gender and age within 5 years prior to stratified division into training and testing datasets. Matching, training, and testing were then performed over 100 iterations to capture the heterogeneity of the dataset.

[0299] FIG. 11C shows results of the sensitivity of the machine-learned approach at LVEDP20 mmHg, LVEDP25 mmHg, and LVDEP30 mmHg in the other secondary analysis. FIGS. 11D, 11E, and 11F each respectively shows ROC curve illustrating diagnostic performance of the machine-learned approach in a propensity matched analysis (age and gender) to predict a LVEDP20 mmHg, LVEDP25 mmHg, and LVDEP30 mmHg.

[0300] Healthcare Provider Portal

[0301] Referring to FIG. 1 (as well as FIGS. 1A and 1B), system 100 (e.g., 100a, 100b), in some embodiments, includes a healthcare provider portal to display an assessment of disease state or condition (e.g., associated with an abnormal LVEDP and/or the presence coronary artery disease and/or pulmonary hypertension, etc.) in a report. In some embodiments, the report is structured as an angiographic-equivalent report. The physician or clinician portal, in some embodiments, is configured to access and retrieve reports from a repository (e.g., a storage area network). The physician or clinician portal and/or repository can be HIPAA-compliant (and equivalent) and compliant with various other privacy requirements. An example healthcare provider portal is provided in U.S. patent application Ser. No. 15/712,104, entitled Method and System for Visualization of Heart Tissue at Risk, which is incorporated by reference herein in its entirety. Although in certain embodiments, the portal is configured for presentation of patient medical information to healthcare professionals, in other embodiments, the healthcare provider portal can be made accessible to patients, other caregivers, family members, researchers, academics, and/or others. This portal may be used for a wide variety of clinical and even research needs in a wide variety of settingsfrom hospitals to emergency rooms, laboratories, battlefield or remote settings, at point of care with a patient's primary care physician or other caregiver, and even the home.

[0302] Example Computing Device

[0303] FIG. 12 shows an example computing environment in which example embodiments of the analysis system 114 and aspects thereof may be implemented in, e.g., a device or devices, among others.

[0304] The computing device environment is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality.

[0305] Numerous other general-purpose or special purpose computing devices environments or configurations may be used. Examples of well-known computing devices, environments, and/or configurations that may be suitable for use include, but are not limited to, personal computers, server computers, handheld or laptop devices, mobile phones, wearable devices, multiprocessor systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, distributed computing environments that include any of the above systems or devices, and the like.

[0306] Computer-executable instructions, such as program modules, being executed by a computer may be used. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Distributed computing environments may be used where tasks are performed by remote processing devices that are linked through a communications network or other data transmission medium. In a distributed computing environment, program modules and other data may be located in both local and remote computer storage media including memory storage devices.

[0307] With reference to FIG. 12, an example system for implementing aspects described herein includes a computing device, such as computing device 1000. In its most basic configuration, computing device 1000 typically includes at least one processing unit 1002 and memory 1004. Depending on the exact configuration and type of computing device, memory 1004 may be volatile (such as random-access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in FIG. 12 by dashed line 1006.

[0308] Computing device 1000 may have additional features/functionality. For example, computing device 1000 may include additional storage (removable and/or non-removable) including, but not limited to, magnetic or optical disks or tape. Such additional storage is illustrated in FIG. 12 by removable storage 1008 and non-removable storage 1010.

[0309] Computing device 1000 typically includes a variety of computer readable media.

[0310] Computer readable media can be any available media that can be accessed by the device 1000 and includes both volatile and non-volatile media, removable and non-removable media.

[0311] Computer storage media include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Memory 1004, removable storage 1008, and non-removable storage 1010 are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device 1000. Any such computer storage media may be part of computing device 1000.

[0312] Computing device 1000 may contain communication connection(s) 1012 that allow the device to communicate with other devices. Computing device 1000 may also have input device(s) 1014 such as a keyboard, mouse, pen, voice input device, touch input device, etc., singly or in combination. Output device(s) 1016 such as a display, speakers, printer, vibratory mechanism, etc. may also be included singly or in combination. All these devices are well known in the art and need not be discussed at length here.

[0313] It should be understood that the various techniques described herein may be implemented in connection with hardware components or software components or, where appropriate, with a combination of both. Illustrative types of hardware components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. The methods and apparatus of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium where, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the presently disclosed subject matter.

[0314] Although example implementations may refer to utilizing aspects of the presently disclosed subject matter in the context of one or more stand-alone computer systems, the subject matter is not so limited, but rather may be implemented in connection with any computing environment, such as a network or distributed computing environment. Still further, aspects of the presently disclosed subject matter may be implemented in or across a plurality of processing chips or devices, and storage may similarly be effected across a plurality of devices. Such devices might include personal computers, network servers, handheld devices, and wearable devices, for example.

[0315] Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[0316] Further examples of processing that may be used with the exemplified method and system are described in: U.S. Pat. No. 9,289,150, entitled Non-invasive Method and System for Characterizing Cardiovascular Systems; U.S. Pat. No. 9,655,536, entitled Non-invasive Method and System for Characterizing Cardiovascular Systems; U.S. Pat. No. 9,968,275, entitled Non-invasive Method and System for Characterizing Cardiovascular Systems; U.S. Pat. No. 8,923,958, entitled System and Method for Evaluating an Electrophysiological Signal; U.S. Pat. No. 9,408,543, entitled Non-invasive Method and System for Characterizing Cardiovascular Systems and All-Cause Mortality and Sudden Cardiac Death Risk; U.S. Pat. No. 9,955,883, entitled Non-invasive Method and System for Characterizing Cardiovascular Systems and All-Cause Mortality and Sudden Cardiac Death Risk; U.S. Pat. No. 9,737,229, entitled Noninvasive Electrocardiographic Method for Estimating Mammalian Cardiac Chamber Size and Mechanical Function; U.S. Pat. No. 10,039,468, entitled Noninvasive Electrocardiographic Method for Estimating Mammalian Cardiac Chamber Size and Mechanical Function; U.S. Pat. No. 9,597,021, entitled Noninvasive Method for Estimating Glucose, Glycosylated Hemoglobin and Other Blood Constituents; U.S. Pat. No. 9,968,265, entitled Method and System for Characterizing Cardiovascular Systems From Single Channel Data; U.S. Pat. No. 9,910,964, entitled Methods and Systems Using Mathematical Analysis and Machine Learning to Diagnose Disease; U.S. Patent Publication No. 2017/0119272, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition; PCT Publication No. WO2017/033164, entitled Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition; U.S. Patent Publication No. 2018/0000371, entitled Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation; PCT Publication No. WO2017/221221, entitled Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation; U.S. Pat. No. 10,292,596, entitled Method and System for Visualization of Heart Tissue at Risk; U.S. patent application Ser. No. 16/402,616, entitled Method and System for Visualization of Heart Tissue at Risk; U.S. Patent Publication No. 2018/0249960, entitled Method and System for Wide-band Phase Gradient Signal Acquisition; U.S. patent application Ser. No. 16/232,801, entitled Method and System to Assess Disease Using Phase Space Volumetric Objects; PCT Application No. IB/2018/060708, entitled Method and System to Assess Disease Using Phase Space Volumetric Objects; U.S. Patent Publication No. US2019/0117164, entitled Methods and Systems of De-Noising Magnetic-Field Based Sensor Data of Electrophysiological Signals; U.S. patent application Ser. No. 16/232,586, entitled Method and System to Assess Disease Using Phase Space Tomography and Machine Learning; PCT Application No. PCT/IB2018/060709, entitled Method and System to Assess Disease Using Phase Space Tomography and Machine Learning; U.S. patent application Ser. No. 16/445,158, entitled Methods and Systems to Quantify and Remove Asynchronous Noise in Biophysical Signals; U.S. patent application Ser. No. 16/725,402, entitled Method and System to Assess Disease Using Phase Space Tomography and Machine Learning (having attorney docket no. 10321-034pv1 and claiming priority to U.S. Patent Provisional Application No. 62/784,984); U.S. patent application Ser. No. 16/429,593, entitled Method and System to Assess Pulmonary Hypertension Using Phase Space Tomography and Machine Learning; U.S. patent application Ser. No. 16/725,416, entitled Method and System for Automated Quantification of Signal Quality; U.S. patent application Ser. No. 16/725,430, entitled Method and System to Configure and Use Neural Network To Assess Medical Disease; U.S. patent application Ser. No. 15/653,433, entitled Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions; U.S. patent application Ser. No. 15/653,431, entitled Discovering Genomes to Use in Machine Learning Techniques, each of which is incorporated by reference herein in its entirety.

[0317] Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including matters of logic with respect to arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification.

[0318] While the methods and systems have been described in connection with certain embodiments and specific examples, it is not intended that the scope be limited to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

[0319] The methods, systems and processes described herein may be used generate stenosis and FFR outputs for use in connection with procedures such as the placement of vascular stents within a vessel such as an artery of a living (e.g., human) subject, and other interventional and surgical system or processes. In one embodiment, the methods, systems and processes described herein can be configured to use the FFR/stenosis outputs to determine and/or modify, intra operation, a number of stents to be placed in a living (e.g., human), including their optimal location of deployment within a given vessel, among others.

[0320] Examples of other biophysical signals that may be analyzed in whole, or in part, using the example methods and systems include, but are not limited to, an electrocardiogram (ECG) data set, an electroencephalogram (EEG) data set, a gamma synchrony signal data set; a respiratory function signal data set; a pulse oximetry signal data set; a perfusion data signal data set; a quasi-periodic biological signal data set; a fetal ECG data set; a blood pressure signal; a cardiac magnetic field data set, and a heart rate signal data set.

[0321] The example analysis can be used in the diagnosis and treatment of cardiac-related pathologies and conditions and/or neurological-related pathologies and conditions, such assessment can be applied to the diagnosis and treatment (including, surgical, minimally invasive, and/or pharmacologic treatment) of any pathologies or conditions in which a biophysical signal is involved in any relevant system of a living body. One example in the cardiac context is the diagnosis of CAD, and other diseases and conditions disclosed herein and its treatment by any number of therapies, alone or in combination, such as the placement of a stent in a coronary artery, performance of an atherectomy, angioplasty, prescription of drug therapy, and/or the prescription of exercise, nutritional and other lifestyle changes, etc. Other cardiac-related pathologies or conditions that may be diagnosed include, e.g., arrhythmia, congestive heart failure, valve failure, pulmonary hypertension (e.g., pulmonary arterial hypertension, pulmonary hypertension due to left heart disease, pulmonary hypertension due to lung disease, pulmonary hypertension due to chronic blood clots, and pulmonary hypertension due to other disease such as blood or other disorders), as well as other cardiac-related pathologies, conditions and/or diseases. Non-limiting examples of neurological-related diseases, pathologies or conditions that may be diagnosed include, e.g., epilepsy, schizophrenia, Parkinson's Disease, Alzheimer's Disease (and all other forms of dementia), autism spectrum (including Asperger syndrome), attention deficit hyperactivity disorder, Huntington's Disease, muscular dystrophy, depression, bipolar disorder, brain/spinal cord tumors (malignant and benign), movement disorders, cognitive impairment, speech impairment, various psychoses, brain/spinal cord/nerve injury, chronic traumatic encephalopathy, cluster headaches, migraine headaches, neuropathy (in its various forms, including peripheral neuropathy), phantom limb/pain, chronic fatigue syndrome, acute and/or chronic pain (including back pain, failed back surgery syndrome, etc.), dyskinesia, anxiety disorders, conditions caused by infections or foreign agents (e.g., Lyme disease, encephalitis, rabies), narcolepsy and other sleep disorders, post-traumatic stress disorder, neurological conditions/effects related to stroke, aneurysms, hemorrhagic injury, etc., tinnitus and other hearing-related diseases/conditions and vision-related diseases/conditions.

[0322] The following patents, applications and publications as listed below and throughout this document are hereby incorporated by reference in their entirety herein.

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