WO/2017/123608 | SYSTEMS AND APPARATUS FOR GAIT MODULATION AND METHODS OF USE |
WO/2021/046342 | SYSTEMS AND METHODS FOR DETECTING SLEEP ACTIVITY |
WO/2018/035818 | METHOD AND DEVICE FOR DETERMINING SLEEPING STATE OF PET |
WOODWARD JONATHAN JAMES (CA)
RAMCHANDANI SHYAMLAL (CA)
EP2285270B1 | 2016-04-20 | |||
EP3769670A1 | 2021-01-27 |
What is claimed is: 1. A method to remove motion-associated artifacts from an acquired measurement signal, wherein the acquired measurement signal is used to non-invasively assess a cardiac disease state or abnormal cardiac condition of a subject, the method comprising: obtaining, by one or more processors, a first biophysical signal data set of the subject comprising a first photoplethysmographic signal and a second photoplethysmographic signal or a cardiac signal; obtaining, by the one or more processors, a second biophysical signal data set of the subject associated with a ballistocardiogram signal, wherein the ballistocardiogram signal are temporally and spatially acquired with respect to the first photoplethysmographic signal, the second photoplethysmographic signal, or the cardiac signal; determining, by the one or more processors, a filtered biophysical-signal data set of the first biophysical signal data set by removing an estimated motion signal determined using the ballistocardiogram signal; determining, by the one or more processors, via a trained classifier model, an estimated value related to a presence of the disease state, abnormal condition, or indication of either; and outputting, via a report and/or display, the estimated value related to the presence of the disease state, abnormal condition, or indication of either, wherein the output is made available to a healthcare provider to assist in a diagnosis of the disease state, abnormal condition, or indication of either, or to direct treatment of disease state, abnormal condition, or indication of either. 2. The method of claim 1, wherein the estimated motion signal comprises an estimated gross movement of the subject. 3. The method of claim 2, further comprising: determining, by the one or more processors, estimated gross subject’s movements during an acquisition of the first biophysical signal data set, wherein the estimated gross subject’s movement is used to reject a portion of the acquired first biophysical signal data set having the estimated gross subject’s movements above a pre-defined threshold value. 4. The method of claim 1, wherein the estimated motion signal comprises assessed vibrations associated with heartbeats of the subject. 5. The method of claim 4, wherein the estimated motion signal is determined by generating a template-signal vector data set characteristic of a representative motion signal pattern of the ballistocardiogram signal. 6. The method of claim 1, wherein the estimated motion signal is used to assess a change in cell potential of respective electrodes used to acquire the cardiac signal. 7. The method of any one of claims 1-6, wherein the subsequent analysis to determine the estimated value for the presence of the cardiac disease state or abnormal cardiac condition comprises: determining, by the one or more processor, one or more synchronicity dynamical features including a first synchronicity feature and a second synchronicity feature, wherein the first and second synchronicity features each characterizes, via a statistical- or dynamical- analysis assessment, one or more synchronicity dynamical properties across multiple heart cycles between (i) the first biophysical signal data set associated with the first and second photoplethysmographic signals or cardiac signal and (ii) the second biophysical signal data set associated with the ballistocardiogram signal. 8. The method of claim 7 or 8, wherein the statistical- or dynamical-analysis assessment is selected from the group consisting of: a statistical- or dynamical-analysis assessment of values of the ballistocardiogram signal at a registration point defined by one or both the first photoplethysmographic signal and the second photoplethysmographic signal; a statistical- or dynamical-analysis assessment of values of the ballistocardiogram signal at a registration point defined by the cardiac signal; a statistical- or dynamical-analysis assessment of values of one of the first photoplethysmographic signal or the second photoplethysmographic signal at a landmark defined in the ballistocardiogram signal; a statistical- or dynamical-analysis assessment of values of the cardiac signal at a landmark defined in the ballistocardiogram signal; a statistical- or dynamical-analysis assessment of time intervals between (a) a first set of landmarks defined in the ballistocardiogram and (b) a second set of landmarks defined in the cardiac signal; a statistical- or dynamical-analysis assessment of time intervals between (a) a first set of landmarks defined between the first photoplethysmographic signal and the second photoplethysmographic signal and (b) a second set of landmarks defined in the ballistocardiogram signal; a statistical- or dynamical-analysis assessment of phase relations between (i) periods of the ballistocardiogram signal and (ii) periods of the cardiac signal; and a statistical- or dynamical-analysis assessment of phase relations between (i) periods of one of the first or second photoplethysmographic signals and (ii) periods of the ballistocardiogram signal. 9. The method of any one of claims 1-8, wherein the subsequent analysis to determine the estimated value for the presence of the cardiac disease state or abnormal cardiac condition comprises: determining, by the one or more processor, one or more dynamical features including a first dynamical feature and a second dynamical feature, wherein the first and second dynamical features each characterizes, via a statistical- or dynamical-analysis assessment, one or more dynamical properties across multiple heart cycles of the second biophysical signal data set associated with the ballistocardiogram signals. 10. The method of any one of claims 1-9, further comprising: determining, by the one or more processors, a Poincaré map of the obtained biophysical signal data set; determining, by the one or more processors, a geometric shape object (e.g., an alpha shape object) of the Poincaré map; and determining, by the one or more processors, one or more geometric properties of the geometric shape object, wherein the one or more determined geometric properties is used in the determination of the estimated value for the presence, non-presence, localization, and/or severity of the disease or condition. 11. The method of any one of claims 1-10, wherein the ballistocardiogram signal is acquired via an accelerometer co-located on a sensor associated with the cardiac signal. 12. The method of any one of claims 1-10, wherein the ballistocardiogram signal is acquired via an acoustic sensor co-located on a sensor associated with the cardiac signal. 13. The method of any one of claims 1-11, wherein the ballistocardiogram signal is acquired via a pressure sensor co-located on a sensor associated with the cardiac signal. 14. The method of any one of claims 1-14, further comprising: obtaining, by the one or more processors, a third biophysical signal data set of the subject associated with a second ballistocardiogram signal, wherein the second ballistocardiogram signal are temporally and spatially acquired with respect to cardiac signal. 15. The method of any one of claims 1-12, wherein the determined 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). 16. The method of any one of claims 1-15, wherein the disease state or 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. 17. A method for non-invasively estimating a disease state, abnormal condition, or indication of either, of a subject, the method comprising: obtaining, by one or more processors, a first biophysical signal data set of the subject comprising (i) a photoplethysmographic signal and/or (ii) a cardiac signal; obtaining, by the one or more processors, a second biophysical signal data set of the subject associated with a ballistocardiogram signal, wherein the ballistocardiogram signal, the photoplethysmographic signal, and the cardiac signal concurrently are acquired via surface sensors placed on the subject; determining, by the one or more processors, over at least a portion of the multiple cardiac cycles, a plurality of registration points in the first biophysical signal data set; determining, by the one or more processors, a value of a feature or parameter comprising (i) a geometric parameter of a Poincaré map or (ii) a statistical parameter of a histogram, wherein the Poincaré map or the histogram is defined by the values of the second biophysical signal data set at the plurality of first biophysical signal data set; determining, by the one or more processors, via a trained classifier model, an estimated value related to a presence of the disease state, abnormal condition, or indication of either; and outputting, via a report and/or display, the estimated value related to the presence of the disease state, abnormal condition, or indication of either, wherein the output is made available to a healthcare provider to assist in a diagnosis of the disease state, abnormal condition, or indication of either, or to direct treatment of disease state, abnormal condition, or indication of either. 18. A method for non-invasively estimating a disease state, abnormal condition, or indication of either, of a subject, the method comprising: obtaining, by the one or more processors, a biophysical signal data set of the subject associated with a ballistocardiographic signal, wherein the ballistocardiographic signal is acquired via surface sensors placed on the subject; determining, by the one or more processors, a value of a ballistocardiographic feature or parameter using the biophysical signal data set; determining, by the one or more processors, via a trained classifier model, an estimated value related to a presence of the disease state, abnormal condition, or indication of either; and outputting, via a report and/or display, the estimated value related to the presence of the disease state, abnormal condition, or indication of either, wherein the output is made available to a healthcare provider to assist in a diagnosis of the disease state, abnormal condition, or indication of either, or to direct treatment of disease state, abnormal condition, or indication of either. 19. The method of claim 17 or 18, comprising any one of the limitations of claims 8-16. 20. The method of claim 17 or 18, wherein the ballistocardiographic feature or parameter includes a quantification of beat-to-beat variations of the ballistocardiogram signal in comparison to another biophysical signal. 21. The method of any one of claims 17-20, wherein the ballistocardiographic feature or parameter includes a quantification of dynamical characteristic including at least one of Lyapunov exponent, correlation dimension, entropy, mutual information, and correlation in relation to another biophysical signal. 22. The method of any one of claims 17-21, wherein the ballistocardiographic feature or parameter includes a quantification of linear characteristic including at least one of peak amplitudes, peak-to-peak distances, and angles between registrations points in the ballistocardiographic signal. 23. The method of any one of claims 17-22, wherein the ballistocardiographic feature or parameter includes a quantification of linear characteristic comprising a 3D vector among the ballistocardiographic signal, a first derivative of the ballistocardiographic signal, and a second derivative of the ballistocardiographic signal. 24. The method of any one of claims 17-23, wherein the ballistocardiographic feature or parameter includes a quantification of at least one of a power spectrum, frequency content, or coherence analysis of the ballistocardiographic signal. 25. The method of any one of claims 1-24, wherein the ballistocardiogram signal is acquired from a plurality of multi-axis accelerometers, wherein each of the plurality of multi- axis accelerometers is co-located to a respective first sensor of the plurality of first sensors. 26. The method of any one of claims 1-25, wherein the multi-axis accelerometers and respective first sensor are integrated into a snap lead. 27. The method of any one of claims 1-26, wherein the snap lead includes a wireless communication transceiver. 28. The method of any one of claims 1-27, wherein the wireless communication transceiver is configured for ultra-wide-band operation. 29. A system comprising: a processor; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to perform any of the methods of claims 1-28. 30. A non-transitory computer-readable medium comprising instructions stored thereon, wherein execution of the instructions by a processor causes the processor to perform any of the methods of claims 1-29. 31. A system comprising: a photoplethysmographic acquisition circuit configured to acquire, from a plurality of first sensors, a plurality of photoplethysmographic signal waveforms each associated with a respective photodiode output; a voltage gradient acquisition circuit configured to acquire, from a plurality of second sensors, a plurality of cardiac signal waveforms at least at a plurality of pre-defined orthogonal locations on a torso of a subject; and a ballistocardiogram acquisition circuit configured, from a plurality of third sensors, a plurality of ballistocardiogram signal waveforms each spatially and temporally synchronized to the plurality of photoplethysmographic signal waveforms or the plurality of cardiac signal waveforms. 32. The system of claim 31 further comprising: a housing, wherein the photoplethysmographic acquisition circuit, the voltage gradient acquisition circuit, and the ballistocardiogram acquisition circuit are located in the housing. 33. The system of claims 31-32 further comprising: the plurality of third sensors comprising a plurality of multi-axis accelerometers, wherein each of the plurality of third sensors is operatively coupled to the ballistocardiogram acquisition circuit and comprises a multi-axis accelerometer of the plurality of multi-axis accelerometers, wherein each of the plurality of multi-axis accelerometers is co-located to a respective first sensor of the plurality of first sensors or to a respective second sensor of the plurality of second sensors. 34. The system of claim 33, wherein the each of the plurality of multi-axis accelerometers is co-located to the respective first sensor comprising a PPG sensor. 35. The system of claim 33 or 34, wherein the each of the plurality of multi-axis accelerometers is co-located to the respective second sensor comprising a voltage gradient sensing electrode (e.g.,^enclosed within each electrode snap of a lead-set associated with the voltage gradient sensing electrode). 36. The system of claims 31-32 further comprising: the plurality of third sensors comprising a plurality of acoustic sensors, wherein each of the plurality of third sensors is operatively coupled to the ballistocardiogram acquisition circuit and comprises an acoustic sensor of the plurality of acoustic sensors, wherein each of the plurality of acoustic sensors is co-located to a respective first sensor of the plurality of first sensors or to a respective second sensor of the plurality of second sensors. 37. The system of claim 36, wherein the each of the plurality of acoustic sensors is co- located to the respective first sensor comprising a PPG sensor. 38. The system of claim 36 or 37, wherein the each of the plurality of multi-axis accelerometers is co-located to the respective second sensor comprising a voltage gradient sensing electrode. 39. The system of claims 31-32 further comprising: the plurality of third sensors comprising a plurality of pressure sensors, wherein each of the plurality of third sensors is operatively coupled to the ballistocardiogram acquisition circuit and comprises a pressure sensor of the plurality of pressure sensors, wherein each of the plurality of acoustic sensors is co-located to a respective second sensor of the plurality of second sensors. 40. The system of any one of claims 31-39, wherein each of the plurality of third sensors is connected to a return conductor and a power supply conductor that each runs through the lead-set associated with the voltage gradient sensing electrode. 41. The system of claim 40, wherein the power supply conductor and return conductor of each respective lead-set encapsulate a first isolated signal conductor associated with a respective third sensor. 42. The system of claim 40 or 41, wherein the power supply conductor and return conductor of each respective lead-set encapsulate a second isolated signal conductor associated with a respective voltage gradient sensing electrode. 43. The system of any one of claims 29-42, wherein the photoplethysmographic acquisition circuit, the voltage gradient acquisition circuit, and the ballistocardiogram acquisition circuit are co-located in an integrated lead set. 44. The system of any one of claims 29-43, wherein the integrated lead set includes a wireless communication transceiver. 45. The method of any one of claims 29-44, wherein the wireless communication transceiver is configured for ultra-wide-band operation. |
[0126] Table IB provides an example list of dynamical system features that are used in the assessment of disease, abnormal condition, or indications of either, that can be implemented in Module(s) 334.
Table 1B
[0127] Lyapunov exponent (“LE BCG”) is the rate of exponential growth of the small initial perturbations of an acoustics, pressure, or accelerometer signal and can be calculated as a representation of fast two nearby trajectories diverging:
[0128] where λ is the LE and δ(t) is the evolution of the initial perturbation δ 0 . In some embodiment, λ is calculated as the average over many points and for a finite time.
[0129] Fractal dimension (“D2 BCG”) of acoustics, pressure, or accelerometer signal can be obtained as:
[0130] where where the function Θ is the Heviside function that acts on two points of the trajectory s and s' and p(s) is the probability density function.
[0131] An alpha shape or convex hull can be generated to encapsulate a phase space data set of either (i) three ballistocardiographic channels or (ii) a ballistocardiographic signal and its first- and second-order derivative. Geometric parameters such as void, volume, perimeters may be extracted as features of the alpha shape or convex hull.
[0132] Additional description of these features, e.g., for correlation dimension, entropy, mutual information, correlation, and outputs of nonlinear filtering, in the context of other biophysical signals, which can be similarly implemented for BCG signals, may be found in U.S. Published Application no. US2020/0397322, entitled “Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals,” which is incorporated by reference herein in its entirety. [0133] Visual and Linear features (Module(s) 506). This third class of features can quantify both the morphological aspects within the waveforms of a biophysical measurement and variations therein, e.g., in cardiac/bipotential signals, ballistocardiographic signals, or photoplethysmographic signals. For photoplethysmographic signals and certain ballistocardiographic signals, morphological aspects may be assessed of waveforms in velocity-photoplethysmographic (VPG) and acceleration- photoplethysmographic (APG) signals of a photoplethysmographic signal. Similarly, a first and second order derivative of an acoustics, pressure, or accelerometer signal may be performed. Examples of these morphologic linear features can include waveform amplitudes, durations, geometric topology, among other morphologies. [0134] Table 1C provide a list of linear features in ballistocardiographic signals that can be used in the assessment of disease, abnormal conditions, or indications of either, that can be implemented in Module(s) 114a. The systolic peaks, pulse base, and minimum peak landmarks of the BCG signal can be determined as listed in Table 1C to which internal angles of a triangle defined by the landmarks may be determined a features or parameters. Table 1C [0135] In addition, in some embodiments, Module 114a can also determine landmarks in the biophysical signals of interest, e.g., corresponding to the atrial depolarization, ventricular depolarization, ventricular repolarization, to identify corresponding data points in the ballistocardiographic signal and calculate features or parameters from such data. The atrial depolarization, ventricular depolarization, ventricular repolarization landmarks in cardiac signals may be calculated and used to identify corresponding data points in the ballistocardiographic signals. These landmarks in the ballistocardiographic signals can then be used to generate vectors based features or parameters. [0136] Visual BCG features can include characteristics of the cardiac vectors at the ballistocardiographic signals (e.g., BCG-based maximal atrial depolarization vector (BCG_MADV), BCG-based atrial repolarization vector (BCG_ARV), BCG-based maximal ventricular depolarization vector (BCG_MVDV), BCG-based initial ventricular depolarization vector (BCG_IVDV), BCG-based terminal ventricular depolarization vector (BCG_TVDV), BCG-based maximal ventricular repolarization vector (BCG_MVRV) in the 3D phase space and within the octants (or subregions) of the 3D space. In some embodiments, visual BCG features can include characteristics of projections of the ballistocardiographic loops (AD, VD, VR) onto the three orthogonal planes and within the quadrants of the respective orthogonal plane. In some embodiments, visual features include characteristics of projections of the BCG vectors onto the three orthogonal planes and within the quadrants of the respective orthogonal plane. [0137] Additional description of these features in the context of other biophysical signals, which can be similarly implemented for BCG signals, may be found in US provisional application no. 63/236,072, entitled “Methods and Systems for Engineering Visual Features from Biophysical Signals for Use in Characterizing Physiological System,” and US provisional application no. 63/235,971, entitled “Methods and Systems for Engineering Photoplethysmographic-Waveform Features from Biophysical Signals for Use in Characterizing Physiological System,” each of which is incorporated by reference herein in its entirety. [0138] Power Spectral and Wavelet Features (Module(s) 508). This fourth class of features can quantify the power spectrum and frequency content of specific regions of the acquired waveform for a biophysical signal such as ballistocardiographic, cardiac, and PPG signals. The analysis may be based on power spectrum analysis and coherence (cross-spectral analysis) analysis. Features can be determined from wavelet analyses of specific regions of the biophysical signals (e.g., ventricular depolarization, ventricular repolarization, and atrial depolarization regions in cardiac/biopotential signals). Corresponding regions in the ballistocardiographic signals can be identified, extracted, and used for the BCG feature computation. In alternative embodiments, regions of analyses in the ballistocardiographic signals are calculated based on landmarks identified in the ballistocardiographic signals. [0139] Power spectral analysis (PSA) assesses signal energy (or power) in the frequency domain by decomposing the time-series signals into their frequency components. Cross- spectral power analysis, also referred to as Coherence Spectral Analysis (CSA), assesses the measures of association between the frequency content of two or more time series. Coherence spectral analysis may be performed between two biophysical signals of the same type (e.g., between two channels of ballistocardiographic signals or between a ballistocardiographic signal and another biophysical signal as described herein). [0140] An example feature (e.g., cohKurt_BCG) calculates the kurtosis within a distribution of calculated coherence between a first and a second BCG spectrum or between a first BCG spectrum and a PPG or cardiac signal. Another example feature (e.g., wtPwave_circularity_BCG_median) calculates the circularity of a high-power spectral region in the atrial depolarization associated regions of a BCG signal. The analysis is performed over multiple cycles to provide a distribution of the results to which the mean of the distribution can be determined. [0141] Table 1D provides a list of power spectral features and wavelet features, respectively, that are used in the assessment of elevated LVEDP that can be implemented in Module(s) 338. Table 1D [0142] Module 114a can pre-process the acquired ballistocardiographic signals, (ii) window the signals, and determine the power spectrum of the windowed signals as the power spectral features or parameters. Module 114a may implement a periodogram to calculate the cumulative power of a given ballistocardiographic signal using an FFT operator with a 10% cosine fraction Tukey window. Module 114a can also determine the coherence of the windowed signals as the cross-power spectral features or parameters. [0143] Additional description of these features in the context of other biophysical signals may be found in U.S. Provisional Patent Application no. 63/235,963, entitled “Methods and Systems for Engineering Power Spectral Features from Biophysical Signals for Use in Characterizing Physiological Systems,” and U.S. Provisional Patent Application no. 63/235,968, entitled “Methods and Systems for Engineering Wavelet-Based Features from Biophysical Signals for Use in Use in Characterizing Physiological Systems,” each of which is incorporated by reference herein in its entirety. [0144] Poincaré and Synchronicity Features (Module(s) 510). This fifth class of features calculates the time difference or time variability between the peaks identified within the waveforms of a biophysical signal, e.g., among ballistocardiographic signals or between ballistocardiographic signals and another biophysical signal type, and uses that calculated time difference as input to a Poincaré analysis, or statistical analysis, to quantify the signal dynamics. Within the Poincaré analysis, the features can be extracted that characterize the synchronicity characteristics between the different modalities of the biophysical signals, e.g., between cardiac/biopotential signals and PPG signals. [0145] Other features or parameters can be calculated from the ballistocardiographic signals and another biophysical signal. Examples of these features or parameters as used in the determination of elevated left end-diastolic pressure and coronary arterial disease are described in U.S. Provisional Patent Application no. 63/235,960, entitled “Method and System to Non- invasively Assess Elevated Left Ventricular End-Diastolic Pressure,” U.S. Publication No. 2020-0397322, entitled “Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals,” U.S. Publication No. 2020-0397324, entitled “Method and System to Assess Disease Using Dynamical Analysis of Cardiac and Photoplethysmographic Signals,” U.S. Provisional Patent Application no. 63/235966, entitled “Methods and Systems for Engineering Respiration Rate-Related Features from Biophysical Signals for Use in Characterizing Physiological Systems,” U.S. Provisional Patent Application no. 63/236193, entitled “Methods and Systems for Engineering Cardiac Waveform Features From Biophysical Signals for Use in Characterizing Physiological Systems,” U.S. Provisional Patent Application no.63/235974, entitled “Methods and Systems for Engineering Conduction Deviation Features From Biophysical Signals for Use in Characterizing Physiological Systems,” and various patent application referenced herein, each of which is incorporated by reference herein in its entirety. [0146] BCG Synchronicity Features [0147] Figs.6-8 provide a description of the synchronicity characteristics between the ballistocardiographic signals and the different modalities of the biophysical signals in accordance with an illustrative embodiment. [0148] Fig.6 shows an operation to extract ballistocardiographic signal data, e.g., comprising acoustic or pressure signals, at registration points defined by photoplethysmographic signals. Figs.7A-7B show Poincare map-based analysis and histogram-based analysis, analysis of the extracted data at the determined registration points of Fig.6. Figs.8A, 8B, and 8C show additional examples to extract ballistocardiographic data at registration points among ballistocardiographic signals, between ballistocardiographic signals and another biophysical signal, and between one or more biophysical signals and the ballistocardiographic signal. [0149] Synchronicity features that are based on dynamics observed in cardiac, photoplethysmographic, ballistocardiographic signals, among other biophysical signals as described herein, may encode the health state of the heart and are used to train a machine learning model for prediction for various disease states or conditions. The electrophysiological activity of the heart is a nonlinear process that, 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 characterized using nonlinear statistics related to the nonlinear dynamics and chaoticity of the heart. In a Poincaré map, the mapping X n+1 = P(X n ) may be defined using triggers (e.g., intersection with Σ), and the set of Poincaré points {X 0 , X 1 ,..., X n } can then be analyzed geometrically and/or statistically to information about the synchronicity between the physiological subsystems of the body. [0150] Fig. 6 shows, in a first plot 602, cross-over landmarks 604a and 604b defined between a first photoplethysmographic signal 606 and a second photoplethysmographic signal 608. A second plot 610 and a third plot 612 each shows the cross-over landmarks 604a and 604b, as registration points, in relation to one of the channels of the acquired ballistocardiographic signal, e.g., comprising pressure signal 614 or acoustic signal 616, to extract values of the ballistocardiographic signal at that same point for synchronicity analysis. The second plot 610 shows the registration points of the PPG signal being used to identify data in the ballistocardiographic signal comprising a pressure measurement. The third plot 612 shows the registration points of the PPG signal being used to identify data in the ballistocardiographic signal comprising an acoustic measurement. In Fig. 6, the x-axis shows time (in seconds), and the y-axis shows the signal amplitude in millivolts (mv). [0151] Fig. 7A shows a histogram 700 of the distribution of values of the ballistocardiographic signal (e.g., 614, 616) at the cross-over landmarks (604a, 604b) of the PPG signals (e.g., 606, 608). Specifically, Fig.7A shows distributions 702, 704 corresponding to amplitude values of the ballistocardiographic signal at the respective first and second sets of crossover landmarks (604a, 604b). In Fig. 7A, the x-axis of the histogram shows signal amplitude (in mV), and the y-axis shows the frequency/count. [0152] Module 114a can generate a histogram (e.g., as generated per Fig. 7A) and extract statistical and geometric properties from the generated histogram. In some embodiments, the extracted histogram features include, for example, but are 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 data 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 normalizing a 2-dimensional histogram of the two time-series data. 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. [0153] In Equation 3, p(., .) is the probability distribution over the specified variables. [0154] Fig. 7B shows another synchronicity analysis comprising a Poincaré map 706 of values of the ballistocardiographic signal (e.g., 614 or 616) at the PPG-based crossover landmarks 604a and 604b. That is, the Poincaré map records the value of the ballistocardiographic signal (e.g., 614 or 616) based on registration points defined by the photoplethysmographic crossover landmarks 604a, 604b. In Fig. 7B, the x-axis and y-axis each shows the difference in amplitude values for the cardiac signal from cycles to cycles (thus, unitless). [0155] In some embodiments, to generate the Poincaré map 706, Module 114a can plot/generate a 2D pair of points [x i , x i+1 ] (e.g., (x 1 , x 2 ), (x 2 , x 3 ), etc.) against the points [x i , x i- 1] (e.g., (x0, x1), (x1, x2), etc.) of the amplitude values of a ballistocardiographic signal at the cross-over landmark points formed between photoplethysmographic signals. [0156] Following the generation of Poincaré map 706, or data object, Module 114a can generate a geometric object 708 from the mapped data. In Fig. 7B, in some embodiments, Module 114a can determine an ellipse (e.g., as data object 708) based on an ellipse fit operation of the data associated with a cluster. Based on the fitted ellipse, Module 114a can determine geometric parameters such as, but not limited to, length of semi-axis “a” (710), semi-axis “b” (712), length along a long axis (714), and length along a short axis (716) as shown in Fig. 7B. Module 114a may extract other parameters such as void area, surface area, porosity, perimeter length, density, among others. Synchronicity between acquired photoplethysmographic signals and a ballistocardiographic signal based on registration points 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, conditions, or indication of such, as described herein. [0157] Table 2A provides a list of example BCG-based synchronicity feature extracted parameters associated with Poincaré map analysis between photoplethysmographic signals and ballistocardiographic signals as their corresponding description. Table 2A [0158] Figs.8A, 8B, and 8C illustrate additional sets of example synchronicity features or parameters between the acquired photoplethysmographic signal(s) and other biophysical signals in accordance with an illustrative embodiment. In Fig. 8A, the crossover points, e.g., as shown in Fig. 6, may be used to define phase differences in relation to a landmark (e.g., peak) in the ballistocardiographic signal. In Fig. 8B, the landmarks (e.g., peaks) in a cardiac signal are used as registration points to identify values in the ballistographic signals to which analysis as described in relation to Figs.7A and 7B may be performed to generate BCG-based synchronicity features or parameters. In Fig. 8C, the landmarks (e.g., peaks) in a ballistocardiographic signal are used as registration points to identify values in a cardiac signal, a PPG signal, and other BCG signals, to which analysis as described in relation to Figs.7A and 7B may be performed to generate additional BCG-based synchronicity features or parameters. [0159] Time-difference-based BCG features. As stated above, Fig.8A shows the crossover points in the photoplethysmographic signals that may be used to define phase differences in relation to a landmark (e.g., peaks) in the ballistocardiographic signal. In Fig. 8A, in a first plot 802, registration points (604a, 604b) comprising first and second crossover landmarks (604a, 604b) between a first photoplethysmographic signal 606 and a second photoplethysmographic signal 608 can be used to identify corresponding signal points (shown as 808a, 808b) of the ballistographic signals in the second plot 804 and the third plot 806 at the same corresponding time or data index. Plots 804 and 806 show the phase difference (shown as “TP” 810, 814 and “TT” 812, 816) of peaks identified in the ballistocardiographic signals 614 and 616, respectively. The extracted TP and TT time values are used in subsequent synchronicity dynamical analysis. The x-axis shows the time domain (in the index count of the data set), and the y-axis shows the acquired amplitude of the signal in millivolts. [0160] Table 2B provides a list of example BCG-based synchronicity features or parameters associated with a Poincaré map analysis between the photoplethysmographic and cardiac signals as well as their corresponding description. Additional BCG-based synchronicity features or parameters may be acquired. Table 2B [0161] Cardiac Registration-Point BCG features. As noted above, Fig. 8B shows the landmarks (e.g., peaks) (818) in a cardiac signal 820 being used as registration points to identify values in the ballistographic signals, e.g., comprising pressure signal 614 and acoustic signal 616, to which analysis as described in relation to Figs.7A and 7B may be performed to generate BCG-based synchronicity features or parameters. In Fig. 8B, in a first plot 822, registration points comprising identified peaks 818 in the cardiac signal 820 can be used to identify values 828 of the ballistographic signals in the second plot 824 and the third plot 826 at the same corresponding time or data index. The registered values 828 are used in subsequent synchronicity dynamical analysis. The x-axis shows the time domain (in the index count of the data set), and the y-axis shows the acquired amplitude of the signals in millivolts. To identify the peaks in the cardiac signal, Module 114a may employ a peak detector, e.g., the Pan-Tompkins detector described in Tompkins W.J., Pan J., “A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering,” BME-32(3) pages 230-236 (1985). [0162] Table 2C provides a list of example synchronicity features OR parameters associated with a Poincaré map analysis between the cardiac signal(s) and the photoplethysmographic signals as well as their corresponding description. Additional synchronicity feature extracted parameters between cardiac signals and ballistocardiogram signals may be acquired. Table 2C [0163] BCG Registration-Point BCG features. As noted above, Fig. 8C shows the landmarks (e.g., peaks) in a ballistocardiographic signal (e.g., pressure signal 614) being used as registration points to identify values in another ballistographic signal, or other biophysical signals, to which analysis as described in relation to Figs. 7A and 7B may be performed to generate BCG-based synchronicity features or parameters. In Fig. 8C, in a first plot 830, registration points comprising identified peaks 832 in the ballistocardiographic signal 614 can be used to identify values 834 of the cardiac signal 820 in the second plot 836, values 838 of one of the photoplethysmographic signals 606, 608 of the second plot 840, and values 842 of another ballistocardiographic signal 616 in the third plot 844 at the same corresponding time or data index. The registered values (e.g., 834, 838, 842) are used in subsequent synchronicity dynamical analysis. The x-axis shows the time domain (in index count of the data set), and the y-axis shows the acquired amplitude of the signals in millivolts. [0164] Artifact Identification and Bi-directional De-Noising between Ballistocardiographic and Cardiac Signals [0165] Fig. 9 is a diagram of a method 900 to assess or quantify skeletal-muscle-related artifact and noise contamination in a biophysical signal in accordance with an illustrative embodiment. In some embodiments, the instant system is configured to identify artifacts in a voltage gradient signal (e.g., cardiac signal 104b) related to both gross motion, recorded as both muscle activation and changes to the half-cell potential, as well as repetitive waveforms, such as that caused by respiration. Recording the ballistocardiographic signal directly at the location of each electrode allows for these motion-related artifacts to be identified within the cardiac signal. Once identified, the noise or artifact may be isolated and removed or used for additional analytical work. Indeed, the assessment or quantification may be used to determine a filtered biophysical-signal data set of a biophysical signal data set by removing an estimated motion signal (e.g., estimated gross subject’s movements, estimated vibrations from the beating of the subject’s heart, or estimated respiration motion or other physical movements) determined using the ballistocardiogram signal, or other biophysical signal described herein. [0166] Furthermore, there is a direct, though non-trivial, relationship between the electrical activation of the myocytes, measured in the VGC, and the forces caused by the contraction of those myocyte cells. The VCG may therefore be used as a source of cardiac-related registration points to identify and isolate the components of the BCG that are related to cardiac forces. By using the strengths of both sources of information in conjunction in this manner, each signal may be separated into sub-components that represent isolated physiological metrics and also sources of electro-mechanical noise in the system. [0167] In the example of Fig.9, method 900 may include, first, detecting (step 802) peaks across the ballistocardiogram signal data set (e.g., 104c). Example algorithms that can be used to detect peaks in the ballistocardiogram signal data set can be used includes, but are not limited to, those described in Etemadi et al., “Wearable ballistocardiogram and seismocardiogram systems for health and performance,” J Appl Physiol 124: 452–461, 2018. Etemadi, for example, discusses the use of the double integration operation to more readily identify the J peak from the noisier base BCG signal. [0168] Method 900 includes using (step 904) the detected peak locations to determine a median peak-to-peak interval (e.g., the median maximum-to-maximum peak for a cardiac signal) and to set a cycle region around each peak (e.g., the maximum peak for the ballistocardiogram signal). For ballistocardiogram signals, the cycle region can be set around the maximum peak. Each of the cycle regions can be stored by a processor in a matrix (also referred to as a “cycle matrix). The cycle matrix may be MxN in which M is the number of detected cycles, and N is 40% of the median peak-to-peak interval (e.g., median max-max intervals for ballistocardiogram signals) in which the 40% of the peak-to-peak interval represents the full temporal “width” of the cycle. Specifically, once the median peak-to-peak interval is known across the dataset, the signal can be divided in half, e.g., to get the “20%” that reaches both forward and backward in time from the peak (e.g., maximum peak) to capture the other waves. [0169] Method 900 includes normalizing (step 906) each cycle to remove any offset. The normalized cardiac signal data set can have a range of “1” and “-1”, though that range can vary depending on the distribution of the data. In some embodiments, the centering operation includes the operation of time-aligning the same feature (e.g., peaks) among the waveforms. Examples of these features include, for ballistocardiogram signals, an initiation of the waves, e.g., determined by a cross-correlation operation, among others. [0170] In other embodiments, each cycle is normalized according to z-scores. Z-score value for a given data point in the template signal vector data set can be calculated as a difference between the value of the given data point and a mean of a set of cycles in which the difference is then normalized by the standard deviation of that given data point to the same indexed data value of the set of cycles. [0171] Method 900 further includes performing, by a processor, a principal component analysis (PCA) on the generated cycle matrix to extract the first two principal components. [0172] Method 900 further includes performing (step 910), by a processor, a clustering operation on the output of the principal component analysis. An example of a clustering operation that can be used includes the DBSCAN algorithm as described in Ester, Kriegel, Sander, Xu, “A density-based algorithm for discovering clustering in large spatial databases with noise,” Proceedings of the Second International Conference on Knowledge Discover and Data Mining. Pages 226-231, which is incorporated by reference herein in its entirety. In some embodiments, the clustering operation is configured to be performed on the first two PCA components, which, in some embodiments, represent the cycles in a two-dimensional space. If the algorithm detects a second dominant cluster representing more than 10% of the total number of cycles, then that signifies the presence of a second dominant cycle morphology, such as premature ventricular contractions. [0173] Method 900 includes extracting (step 912), by a processor, a representative cycle based on all, or some of, the cycles that correspond to the dominant PCA cluster, e.g., as detected by DBSCAN. The representative cycle may be extracted in one or several ways, each with different characteristics. In some embodiments, each of the data points in the representative cycle will embody an underlying distribution, where that distribution is composed of that time-point in all the M cycles. For example, taking the mean (across all M points, for each N) has a low-pass filtering effect (removing both high-frequency information and noise), while taking the median preserves high-frequency information in a non-linear fashion. The differing impact of the compression technique, mean vs. median, is accounted for by varying the underlying distributions. If the M points are normally distributed, then the mean and median have the same result but start to differ with more complex distributions, such as those with non-zero skewness, and especially in combination with negative kurtosis or in the presence of multimodality. [0174] Fig. 10 is a diagram of an example method 1000 to quantify, by a processor, skeletal-muscle-related artifact noise contamination in an acquired biophysical signal in accordance with an illustrative embodiment. [0175] Method 1000 includes steps 902-912 as discussed in relation to Fig. 9 and further includes the step of quantifying, by a processor, the distribution of differences between the determined representative cycle data set and the raw signal data set(s). [0176] Method 1000 further includes comparing each detected cycle in the raw signal data set the cycle to the representative cycle data set. The comparison is performed by, first, phase- aligning (step 1002) the representative cycle with each of the cycles under examination. In some embodiments, a method such as finding the maximum of the cross-correlation is used. [0177] The comparison further includes determining (step 1004) a difference between the representative cycle data set and the phase-aligned cycle under examination. In some embodiments, a method such as the correlation between the two signals is used. In other embodiments, a median absolute error is used. In yet other embodiments, a mean absolute error is used. If there is more than one representative cycle data set (as, e.g., detected through clustering on the two-dimensional PCA output), then the corresponding representative cycle data set that most matches a given cycle is used. [0178] The comparison further includes differentiating (step 1006) outlying cycles and inlying cycles based on a difference score determined, e.g., using a distribution-based filter. In some embodiments, the distribution-based filter is configured to identify cycles having a standard deviation greater than one from the mean. In some embodiments, a plot of the distribution of difference scores is determined based on a comparison of the representative cycle data set and each of the evaluated cycles as a function of the cycle index. The inlying cycles may be identified be within one standard deviation of the mean of the distribution, and the outlying cycles are identified to be outside the one standard deviation region from the mean. A final assessment of the contamination of the biophysical signal by the skeletal-muscle-related noise can be performed by taking a representative value of the inlying difference scores, such as the mean or the median. [0179] Experimental Results and Examples [0180] Several development studies have been conducted to develop feature sets, and in turn, algorithms that can be used to estimate the presence or non-presence, severity, or localization of diseases, medical condition, or an indication of either. In one study, algorithms were developed for the non-invasive assessment of abnormal or elevated LVEDP. As noted above, abnormal or elevated LVEDP is an indicator of heart failure in its various forms. In another development study, algorithms and features were developed for the non-invasive assessment of coronary artery disease. [0181] As part of these two development studies, clinical data were collected from adult human patients using a biophysical signal capture system and according to protocols described in relation to Fig.2. The subjects underwent cardiac catheterization (the current “gold standard” tests for CAD and abnormal LVEDP evaluation) following the signal acquisition, and the catheterization results were evaluated for CAD labels and elevated LVEDP values. The collected data were stratified into separate cohorts: one for feature/algorithm development and the other for their validation. [0182] Within the feature development phases, features were developed to extract characteristics in an analytical framework from biopotential signals (as an example of the cardiac signals discussed herein) and photo-absorption signals (as examples of the hemodynamic or photoplethysmographic discussed herein) that are intended to represent properties of the cardiovascular system. Corresponding classifiers were also developed using classifier models, linear models (e.g., Elastic Net), decision tree models (XGB Classifier, random forest models, etc.), support vector machine models, and neural network models to non-invasively estimate the presence of an elevated or abnormal LVEDP. Univariate feature selection assessments and cross-validation operations were performed to identify features for use in machine learning models (e.g., classifiers) for the specific disease indication of interest. Further description of the machine learning training and assessment are described in a U.S. provisional patent application concurrently filed herewith entitled “Method and System to Non- Invasively Assess Elevated Left Ventricular End-Diastolic Pressure” having attorney docket no.10321-048pv1, which is hereby incorporated by reference herein in its entirety. [0183] Further development studies can be conducted to collect and evaluate the performance of BCG features, including those discussed herein. [0184] Discussion and Additional Examples [0185] It has been previously demonstrated that the synchronous capture and subsequent analysis of two types of biometric, the photoplethysmogram (PPG) waveform at the finger and a 3-axis bipolar voltage gradient collected about the thorax, leads to improved assessment of cardiovascular disease over the sole use of one or the other. Such analysis has been shown to have clinical utility in the assessment of coronary artery disease (CAD) as well as in abnormal left ventricular end-diastolic pressure (LVEDP), which is a marker of heart failure (HF). [0186] The first of these biometrics, the PPG, measures waveforms associated with the flow of blood through the vascular system. It can be used to calculate measurements such as oxygen saturation, as well as estimate parameters such as blood pressure. Physiologically it can be seen that embedded in the waveform(s) will be information about the dynamic flow of blood caused by the pumping action of the heart, subsequently modified by the individual’s arterial system. [0187] The voltage gradients measured around the thorax are primarily generated by the electrical activity of the heart as each myocyte is triggered to contract and subsequently released. Physiologically it can be seen that embedded in the three-dimensional voltage gradient is information about the electronic conduction pathways available in the heart, as well as physical properties, such as myocardial tissue dimensions and scarring, that may define and impact these pathways. [0188] The BCG measurement reflects the flow of blood through the entire body. This is most directly measured by having the subject lie on a modified bed such that the weight supported by various parts of the bed can be monitored. As blood moves back and forth in the longitudinal direction between head and feet, the volume of blood in each location will change dynamically and be reflected in the weight measured at each location on the bed. This method is, however, cumbersome, requiring a bed, and so recent research has focused on estimations of the BCG using wearable devices. The seismocardiogram (SCG) refers more specifically to the vibrations recorded by sensors mounted close to the heart. However, in practice, the term BCG is now used to cover both. [0189] Combining the information from synchronized measurements of these three types of physiologically defined metrics, among others, can isolate and reveal information that may otherwise be hidden or inaccessible. For example, the time difference between the peak of the electrical waveform and the peak of the flow waveform will, in part, be directly related to the distance between the two points and the velocity of the blood pressure wave. [0190] Etamadi and Inan have reviewed methods of capture, processing, and interpretation of BCG and SCG in the context of synchronously obtained ECG and PPG signals, primarily to provide estimates of pressure-related metrics, such as cardiac output, pulse transit time (PTT), and diastolic blood pressure. The exemplary system and method can generate features or parameters for machine learning from ballistocardiographic, photoplethysmographic, and cardiac time-series data, as well as other biophysical signals, to assess disease or abnormal states, or indicators of either, of the cardiovascular system and various other physiological systems described herein. [0191] The instant system, in some embodiments, can employ a modified PSR device that is configured to synchronously measure photoplethysmographic and three-dimensional voltage gradient signals. The instant system may use these individually and in combination to generate features that are then used in machine learning campaigns to predict various cardiovascular diseases. [0192] Whilst previous research has focused on the use of ballistocardiographic signals to estimate various cardiovascular pressure-related parameters, the instant system and associated analytical framework cover the synchronous measurement of ballistocardiographic signals using accelerometers embedded within the electrode snaps of a lead set employed to measure voltage gradients (VCG). [0193] In some embodiment, the accelerometry data are used to assess for gross patient movements that may interfere with the collection of other data, such as the cardiac signals and/or photoplethysmographic signals. In some embodiment, the accelerometry data are used to measure the vibrations caused by the beating of the heart (SCG/BCG). In some embodiment, the accelerometry data are used to measure other physical motion parameters, including respiration, that may be used to further improve the fidelity of the cardiac signals by removing artifacts in the signal caused by, for example, changes in the half-cell potential of the electrodes. [0194] Whilst this embodiment describes the use of embedded accelerometers to measure motion-based parameters, a similar apparatus may substitute or add other types of sensors embedded into the lead-set snaps. One such example would be an acoustic sensor employing a modified electrode capable of acting as an acoustic channel as well as an electrical conductor. [0195] In some embodiments, a multi-axis accelerometer is enclosed within each electrode snap of a lead-set. The accelerometer is powered from the body of the recording device using ground and live conductors run through the leads. In effect, the power supply conductors are used as additional layers of EMI shielding in the lead construction. An additional signal connection is also run back to the body of the recording device through each lead. [0196] It is contemplated that the instant system may employ sensors that are each mounted directly atop an electrode but independently powered – i.e., not connected to a central device body using a lead-set. In some embodiment, the sensors may, for example, be wirelessly synchronized and offload their data to a separate device, such as a cellphone or hub unit. Each of the three areas discussed above applies equally to acoustic signals and, therefore, the combination of any co-located, synchronized set of two or more sensor types selected from acoustic, ballistic, electrical, or magnetic sensors. [0197] In place of an accelerometer, or in addition, a pressure sensor may be mounted within the gel of an electrode. This would measure primarily forces applied in the direction perpendicular to the surface of the skin. It would be more closely aligned with the changes in the half-cell potential of the electrode, which would provide advantages for de-noising of the cardiac signal. [0198] 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. [0199] 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. [0200] 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. The clinical evaluation system and method discussed herein may be employed to make, or to assist a physician or other healthcare provider in making, noninvasive diagnoses or determinations of the presence or non-presence and/or severity of other diseases and/or conditions, such as, e.g., coronary artery disease, pulmonary hypertension and other pathologies as described herein using similar or other development approaches. In addition, the example clinical evaluation system and method can be used in the diagnosis and treatment of other cardiac-related pathologies and conditions as well as 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, the 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 diseases 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. [0201] In addition, the clinical evaluation system described herein may be configured to analyze biophysical signals such as an electrocardiogram (ECG), electroencephalogram (EEG), gamma synchrony, respiratory function signals, pulse oximetry signals, perfusion data signals; quasi-periodic biological signals, fetal ECG signals, blood pressure signals; cardiac magnetic field signals, heart rate signals, among others. [0202] Further examples of processing that may be used with the exemplified method and system disclosed herein are described in: U.S. Patent nos. 9,289,150; 9,655,536; 9,968,275; 8,923,958; 9,408,543; 9,955,883; 9,737,229; 10,039,468; 9,597,021; 9,968,265; 9,910,964; 10,672,518; 10,566,091; 10,566,092; 10,542,897; 10,362,950; 10,292,596; 10,806,349; U.S. Patent Publication nos. 2020/0335217; 2020/0229724; 2019/0214137; 2018/0249960; 2019/0200893; 2019/0384757; 2020/0211713; 2019/0365265; 2020/0205739; 2020/0205745; 2019/0026430; 2019/0026431; PCT Publication nos. 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