METHOD AND SYSTEM TO CHARACTERIZE DISEASE USING PARAMETRIC FEATURES OF A VOLUMETRIC OBJECT AND MACHINE LEARNING
20200211713 ยท 2020-07-02
Inventors
- Ian Shadforth (Morrisville, NC)
- Timothy William Fawcett Burton (Toronto, CA)
- Sunny Gupta (Belleville, CA)
- Farhad Fathieh (North York, CA)
Cpc classification
Y02A90/10
GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
G16H50/20
PHYSICS
G16H10/60
PHYSICS
A61B5/7225
HUMAN NECESSITIES
G06N7/01
PHYSICS
G16H15/00
PHYSICS
A61B5/02007
HUMAN NECESSITIES
G06N3/126
PHYSICS
International classification
A61B5/00
HUMAN NECESSITIES
A61B5/02
HUMAN NECESSITIES
G06N7/00
PHYSICS
G16H10/60
PHYSICS
G16H15/00
PHYSICS
Abstract
The exemplified methods and systems employs non-invasively acquired biophysical measurements of a subject in a residue analysis that is structured as a three-dimensional volumetric object to which machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object may be derived and used for in the training and/or prediction of a disease state. The system generates a residue model from a point-cloud residue generated from an analysis of the plurality of biophysical signal data sets. The system generates a three-dimensional volumetric object from the point-cloud residue from which machine extractable features associated with the point-cloud residue maybe extracted.
Claims
1. A method for non-invasively assessing a representation of a physiological system in which the representation is indicative of a disease state of a subject, the method comprising: obtaining, by one or more processors, a plurality of biophysical signal data sets of a subject; generating, by the one or more processors, a residue model from an analysis of the plurality of biophysical signal data sets; generating, by the one or more processors, a three-dimensional volumetric object from a point-cloud residue, wherein the point-cloud residue comprises a plurality of vertices defined in a three-dimensional phase space of the plurality of biophysical signal data sets; and determining, by the one or more processors, machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object, wherein the one or more machine extractable features are used as an indicator of a disease state.
2. The method of claim 1, wherein the step of generating the three-dimensional volumetric object comprises: performing a triangulation operation on the point-cloud residue of the plurality of biophysical signal data sets, wherein the triangulation operation is selected from the group consisting of Delaunay triangulation, Mesh generation, Alpha Hull triangulation, and Convex Hull triangulation.
3. The method of claim 1, wherein the machine extractable features are used in a machine-trained estimation of presence and/or non-presence of significant coronary artery disease.
4. The method of claim 1, wherein the machine extractable features are selected from the group consisting of a 3D object volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value.
5. The method of claim 1, further comprising: generating a contour data set for each tomographic image of the set of tomographic images, wherein the contour data are presented for the assessment of presence and/or non-presence of significant coronary artery disease.
6. The method of claim 1, wherein the acquired plurality of biophysical signal data sets are derived from measurements acquired via a noninvasive equipment configured to measure properties selected from the group consisting of electric properties, magnetic properties, acoustic properties, impedance properties, and reflectance properties of a physiological system.
7. The method of claim 1 further comprising: removing, by the one or more processors, a baseline wandering trend from the acquired data prior to generating the plurality of models.
8. The method of claim 1 comprising: causing, by the one or more processors, generation of a visualization of generated volumetric object as a three-dimensional object, wherein the three-dimensional object is rendered and displayed at a display of a computing device and/or presented in a report.
9. The method of claim 1, wherein each of the acquired biophysical signal data sets comprises a wide-band phase gradient biopotential signal data set that is simultaneously acquired at a sampling rate selected from the group consisting of about 1 kHz, about 2 kHz, about 3 kHz, about 4 kHz, about 5 kHz, about 6 kHz, about 7 kHz, about 8 kHz, about 9 kHz, about 10 kHz, and greater than 10 kHz.
10. The method of claim 1, wherein the residue model is generated by: a subtraction operation of the acquired biophysical signal data sets and a data set generated from the analysis of the plurality of biophysical signal data sets.
11. The method of claim 1, wherein the analysis of the plurality of biophysical signal data sets comprises a quasi-periodic analysis of the frequency components of the plurality of biophysical signal data sets.
12. The method of claim 1, wherein the analysis of the plurality of biophysical signal data sets comprises a chaotic analysis of the frequency components of the plurality of biophysical signal data sets.
13. The method of claim 1, wherein the analysis of the plurality of biophysical signal data sets comprises a phase analysis of the plurality of biophysical signal data sets.
14. A system comprising: a processor; and a memory having instructions thereon, wherein the instructions when executed by the processor, cause the processor to: obtain a plurality of biophysical signal data sets of a subject; generate a residue model from an analysis of the plurality of biophysical signal data sets; generate a three-dimensional volumetric object from a point-cloud residue, wherein the point-cloud residue comprises a plurality of vertices defined in a three-dimensional phase space of the plurality of biophysical signal data sets; and determine machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object, wherein the one or more machine extractable features are used as an indicator of a disease state.
15. The system of claim 14, wherein the instruction to generate the three-dimensional volumetric object comprises: instructions to perform a triangulation operation on the point-cloud residue of the plurality of biophysical signal data sets, wherein the triangulation operation is selected from the group consisting of Delaunay triangulation, Mesh generation, Alpha Hull triangulation, and Convex Hull triangulation.
16. The system of claim 14, wherein the machine extractable features are selected from the group consisting of a 3D object volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value.
17. The system of claim 14 further comprising: a noninvasive equipment configured to measure properties selected from the group consisting of electric properties, magnetic properties, acoustic properties, impedance properties, and reflectance properties of a physiological system.
18. The system of claim 14, wherein the noninvasive equipment comprises a phase space recorder and/or an optical photoplethysmograph system.
19. The system of claim 14, wherein the analysis of the plurality of biophysical signal data sets comprises at least one of: a quasi-periodic analysis of the frequency components of the plurality of biophysical signal data sets, a chaotic analysis of the frequency components of the plurality of biophysical signal data sets, and a phase analysis of the plurality of biophysical signal data sets.
20. A non-transitory computer readable medium having instructions stored thereon, wherein execution of the instructions, cause the processor to: obtain a plurality of biophysical signal data sets of a subject; generate a residue model from an analysis of the plurality of biophysical signal data sets; generate a three-dimensional volumetric object from a point-cloud residue, wherein the point-cloud residue comprises a plurality of vertices defined in a three-dimensional phase space of the plurality of biophysical signal data sets; and determine machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object, wherein the one or more machine extractable features are used as an indicator of a disease state.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0039] 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. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
[0040] Embodiments may be better understood from the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures:
[0041]
[0042]
[0043]
[0044]
[0045]
[0046]
[0047]
[0048]
[0049]
[0050]
[0051]
[0052]
[0053]
[0054]
[0055]
[0056]
[0057]
[0058]
[0059]
[0060]
[0061]
[0062]
[0063]
DETAILED SPECIFICATION
[0064] 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.
[0065] While the present disclosure is directed to the beneficial assessment of biophysical signals 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 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.
[0066] Example System
[0067]
[0068] The system 100a generates a three-dimensional representation (or equivalent two-dimensional representation) of the residue model within a set of acquired biophysical signals collected by a measurement system 102 (also referred to as phase space recorder or PSR device). The term generally refers to a methodology that directly represent a physiological system, or sub-system of interest, as a multidimensional space in which each of the axes corresponds to one of the variables required to represent the state of the system. Residue model of other biophysical signal types (e.g., waveforms of photoplethysmographic signals) as discussed herein may be generated.
[0069] In
[0070] As shown in
[0071] In some embodiments, the Pre-Processing module 116 is configured to perform a baseline wander removal operation (e.g., phase-linear baseline wander removal operation), a normalization operation (e.g., phase-linear signal normalization operation), and/or a down-sampling operation (e.g., phase-linear down-sampling operation). As used herein, the term phase linear generally refers to phase-neutral filters and operators that do not introduce any phase distortions into a signal, thereby preserving the phase information in the signal for subsequent analysis (e.g., phase space analysis). An example of the pre-processed biophysical signal data set 118 in phase space for a cardiac signal is shown in plot 117. The phase linear operation may be performed with respect to multiple channels of a same first signal type in conjunction with a second set of signals concurrently acquired with the first signal type. For example, acquisition between wide-band phase gradient cardiac signals may be performed with pre-defined phase with acquisition of waveforms of photoplethysmographic signals.
Residue Model Example #1: Chaotic Analysis
[0072] In some embodiments, the biophysical-signal assessment system 110 as configured in
[0073] In some embodiments, the images 126 of volumetric object may be generated which can be colorized (as, for example, shown in
Residue Model Example #2: Phase Analysis
[0074] In some embodiments, the residue model is generated from a residue subspace dataset determined by generating a first wavelet signal dataset by performing a first wavelet operation (via, e.g., a first phase linear wavelet operator) on data derived from the plurality of wide-band gradient signals; generating a second wavelet signal dataset by performing a second wavelet operation (via, e.g., a second phase linear wavelet operator) on the first wavelet signal data; and subtracting values of the first wavelet signal dataset from values of the second wavelet signal dataset to generate the residue subspace dataset, wherein the residue subspace dataset comprises a three-dimensional phase space dataset in a space-time domain.
[0075] Further description of such residue model is described in U.S. Pat. No. 10,362,950, which is incorporated by reference herein in its entirety.
[0076] Referring back to
[0077] In some embodiments, and as shown in
[0078] The anatomical mapping report 130, in some embodiments, includes one or more depictions of a rotatable and optionally scalable three-dimensional anatomical map of cardiac regions of affected myocardium. The anatomical mapping report 130, in some embodiments, is configured to display and switch between a set of one or more three-dimensional views and/or a set of two-dimensional views of a model having identified regions of myocardium. The coronary tree report 130, in some embodiments, includes one or more two-dimensional view of the major coronary artery. The 17-segment report 130, in some embodiments, includes one or more two-dimensional 17-segment views of corresponding regions of myocardium. In each of the report, the value (e.g., 134) that indicates presence of cardiac disease or condition at a location in the myocardium, as well as a label indicating presence of cardiac disease (e.g., 134), may be rendered as both static and dynamic visualization elements that indicates area of predicted blockage, for example, with color highlights of a region of affected myocardium and with an animation sequence that highlight region of affected coronary arter(ies). In some embodiments, each of the report includes textual label to indicate presence or non-presence of cardiac disease (e.g., presence of significant coronary artery disease) as well as a textual label to indicate presence (i.e., location) of the cardiac disease in a given coronary artery disease.
[0079] In the context of cardiovascular systems, in some embodiments, the healthcare provider portal (and corresponding user interface) 128 is configured to present summary information visualizations of myocardial tissue that identifies myocardium at risk and/or coronary arteries that are blocked. The user interface can be a graphical user interface (GUI) with a touch- or pre-touch sensitive screen with input capability. The user interface can be used, for example, to direct diagnostics and treatment of a patient and/or to assess patients in a study. The visualizations, for a given report of a study, may include multiple depictions of a rotatable three-dimensional anatomical map of cardiac regions of affected myocardium, a corresponding two-dimensional view of the major coronary arteries, and a corresponding two-dimensional 17-segment view of the major coronary arteries to facilitate interpretation and assessment of architectural features of the myocardium for characterizing abnormalities in the heart and in cardiovascular functions.
[0080] Indeed, in some embodiments, the three-dimensional volumetric object generated from a residue analysis, and parameters derived therefrom, may be interpreted manually or used as part of a machine learned classifier or predictor module that may be configured to assist in the determination of the presence or absence of disease or condition. Such a module may be local or remote to the assessment system 110. In some embodiments, and as shown in
[0081] In an example, an analysis of the characteristics of a physiological system is performed from a set of acquired biophysical signals. In the context of the cardiovascular system, cardiac phase signals can exhibit complex nonlinear variability that may not be efficiently described by traditional modeling techniques. Without wishing to be bound to a particular theory, it is believed that in a nonlinear system such as the cardiovascular system there is a cascade effect whereby components of the system act upon and amplify other components. In the heart, the conduction system and the heart muscle itself may act upon/affect each otherand in turn affect and are affected by the vasculature. In a chaotic system, small changes in initial conditions may be amplified in the same or similar manner, resulting in behavior that seems (e.g., to a person) unfathomably complex. That is, it may appear to be random. As described in V. Sharma, Deterministic Chaos and Fractal Complexity in the Dynamics of Cardiovascular Behavior: Perspectives on a New Frontier, Open Cardiovasc Med J., pp 110-12 3 (September 2009), the observed chaotic component may not be truly random, and can include encoding of a type of biological memory, which can allow the physiological system to revisit previous states without requiring that these states be directly encoded or repetitious. In contrast, truly random behavior contains no such biological memory or state and is instead associated with a decline in system, and hence biological, health. Without wishing to be bound to a particular theory, it is believed that a decline in chaotic behavior may diminish the ability of a given physiological system to adapt to a stimulus subjected to the system, thereby causing it to become periodic, which can be attributed to being deleterious to health. Similar investigations and observations have been made about the brain and other physiological systems. As later discussed, it can be readily observed that neurological signal data sets similarly contain both quasi-periodic signals and chaotic signals that can be analyzed using the exemplary analysis.
[0082] Three-dimensional volumetric objects may be used as a functional representation of a residue analysis of the physiological system.
[0083] In some embodiments, an analysis entails first modelling components of the acquired signals to a model of the signals and then subtracting the modelled signal data set from the acquired biophysical signal data set to determine a residue point cloud as the functional representation of the characteristics of the physiological system. The residue point-cloud model remaining, say, once the modelled signal has been subtracted from the input signal contains none of the traditional landmarks of the acquired biophysical signal (for a cardiac biophysical signal, the residue contains none of the traditional landmarks, say, as observed in conventional electrocardiogram (ECG) trace, which by its nature is a quasi-periodic signal).
[0084] When the point-cloud residue is presented as a volumetric object, features sets derived from the object can be used readily in a classification or a machine learned operation to estimate/predict and display contextual information on a patient's health, including the status of specific physiologic system health (e.g., cardiac health, a brain/neurological health, pulmonary health, and other biological system health). Volumetric object of residue point cloud data set can be synthesized and displayed via shapes and colors to represent the electrical and/or functional behavior and/or characteristics of the heart or other physiological systems.
[0085] Volumetric Object of a Residue Point-Cloud Model of an Analysis of a Physiological System
[0086]
[0087] Per
[0088]
[0089] In contrast,
[0090] Per
[0091] Example Method to Construct a Three-Dimensional Volumetric Object from a Point-Cloud Residue
[0092]
[0093] The method 600 includes acquiring at step 602 a biophysical data set, e.g., from the measurement system 102 or from a data repository having received the biophysical data set from the measurement system 102, e.g., as described in relation to
[0094] In the neurological context, measurement system 102 is configured to capture neurological-related biopotential or electrophysiological signals of a living 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 or other signal types (e.g., a current signal, an impedance signal, a magnetic signal, an ultrasound, an optical signal, an ultrasound or acoustic signal, etc.). Examples of measurement system 102 are described in U.S. Publication No. 2017/0119272 and in U.S. Publication No. 2018/0249960, each of which is incorporated by reference herein in its entirety.
[0095] In some embodiments, measurement system 102 is configured to capture wide-band biopotential biophysical phase gradient signals as unfiltered 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, 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 microseconds, and in other embodiments, having a temporal skew or lag of not more than about 10 femtoseconds. Notably, the exemplified system minimizes 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.
[0096] In some embodiments, six simultaneously sampled signals are captured from a resting subject as a raw differential channel signal data set (e.g., comprising channels that may be called ORTH1, ORTH2, and ORTH3) in which the signals embed the inter-lead timing and phase information of the acquired signals specific to the subject. Geometrical contrast arising from the interference in the phase plane of the depolarization wave with the other orthogonal leads can be used to facilitate superimposition of phase space information on a three-dimensional representation of, in one example, the heart. Noiseless subspaces further facilitate the observation of the phase of these waves. That is, the phase of the orthogonal leads carries the information about the structure and generates geometrical contrast in the image. Phase-contrast takes advantage of the fact that different bioelectric structures have different impedances, and so spectral and non-spectral conduction delays and bends the trajectory of phase space orbit through the heart by different amounts. In the cardiovascular context, these small changes in trajectory can be normalized and quantified beat-to-beat and corrected for abnormal or poor lead placement, and the normalized phase space integrals can be mapped to a geometric mesh for visualization.
[0097] In some embodiments, the non-invasive measurement system 102 is configured to sample a biophysical signal (e.g., bipolar biopotential signals) at about a sampling rate greater than about 1 kHz (e.g., about 8 kHz) for each of three differential channels orthogonally placed on a subject for a duration between about 30 and about 1400 seconds, e.g., for about 210 seconds. Other durations and sampling rates may be used.
[0098]
[0099]
[0100]
[0101] In addition to acquisition of a cardiac signal data set, the exemplified system 100a may be used to acquire neurological signal data sets (e.g., wide-band phase-gradient cerebral signal data sets).
[0102]
[0103] The phase space representation 1004 of the neurological signal data sets 1002a, 1002b, 1002c presents in each of the axes (shown as X, Y, and Z) the corresponding wide-band phase-gradient neurological signal data sets 1002a, 1002b, 1002c. It can be readily observed that the neurological signal data sets contain both quasi-periodic signals and chaotic signals and thus can be analyzed using techniques as disclosed herein that facilitate the modeling and analysis of quasi-periodic and chaotic signals for wide-band phase-gradient cardiac signals.
[0104] Referring to
[0105] In the context of a cardiac signal, the baseline wander removal operation 602 is implemented, in some embodiments, as a phase-linear 2.sup.nd order high-pass filter (e.g., a second-order forward-reverse high-pass filter having a cut-off frequency at 0.67 Hz). The forward and reverse operation ensures that the resulting pre-processed biophysical-signal data set 118 is phase-linear. Other phase-linear operations be usede.g., based on wavelet filters, etc.
[0106] In other embodiments, a multi-stage moving average filter (median filter, e.g., with an order of 1500 milliseconds, smoothed with a 1-Hz low-pass filter) is used to extract a bias signal from each of the input raw differential channel signals. The bias is then removed from the signals by subtracting estimations of the signals using maximums of probability densities calculated with a kernel smoothing function.
[0107] In some embodiments, the signal is run though a signal quality test where the relevant output is the time-indices of the signal appropriate (of sufficient quality) for analysis. An example of the signal-quality test is described in U.S. Provisional Appl. No. 62/784,962, titled Method and System for Automated Quantification of Signal Quality, which is filed on Dec. 26, 2018, and incorporated by reference herein in its entirety.
[0108] In some embodiments, the method 600 further includes down-sampling the input signal or the pre-processed signal (e.g., to 1 kHz). In some embodiments, the down-sampling operation is an averaging operator or a decimation operator.
[0109] In some embodiments, the method further includes normalizing the input acquired biophysical signal data set 108 or the pre-processed signal 118. Similar types of down-sampling, baseline wander, and/or normalization operation can be applied to other biophysical-signal data sets.
[0110] In some embodiments, the method includes using only a portion of the acquired biophysical signal data set, e.g., that portion acquired after or before a pre-defined time or data set offset (e.g., after the first 31 seconds). It is observed, in some embodiments, that such operations can minimize and/or reduce motion artifacts (and therefore improve signal quality) that can be introduced by movement of a subject during the start of a measurement acquisition, therefore resulting in improved signal quality. It is also observed that such operations can minimize and/or reduce distortions (and therefore improve signal quality) in the measurement that can be attributed to probe placement and contacts and which is generally observed to reduce over the course of the measurement acquisition as the probe settles, also resulting in improved signal quality. Other time or data set offset techniques can be used; e.g., those based on quantification of noise in the acquired biophysical data set which may be the result of or associated with the biophysical signal acquisition protocol (instructions), types of probes or electrodes used, and the types and/or configurations of components such as cables for the transmission of signals, the biophysical signal measurement system, the biophysical signal acquisition space/environment, proximity to other medical equipment, etc.
[0111] Method 600 next includes at step 606 reconstructing a residue point cloud model. In some embodiments, the method decomposes the pre-processed biophysical-signal data set 118 into a linear combination of a set of selected candidate basis functions. As noted above, to isolate the deterministic behavior of the physiological system from other types of physiological behavior, a residue point cloud model/data set 125 is generated, e.g., by subtracting the pre-processed biophysical-signal data set 118, e.g., with the noiseless model. As discussed above, other techniques of generating a residue point cloud model may be employed.
[0112] In some embodiments, the modeling module has a greater than 99% accuracy. In some embodiments, the modeling module has a greater than 99.9% accuracy. In some embodiments, the modeling module has a greater than 99.99% accuracy. In some embodiments, the modeling module has a greater than 99.999% accuracy. In some embodiments, the modeling module has a greater than 99.9999% accuracy.
[0113] In some embodiments, a sparse-approximation signal decomposition algorithm is used that is configured to iteratively and recursively select candidate basis functions to add to the model based on a multi-dimensional (e.g., three dimensions) assessment of mutual information from all acquired channels (e.g., channels ORTH1, ORTH2, and ORTH3). That is, one or more terms (e.g., sine terms, cosine terms, complex exponential terms, etc.) are selected, by the processor, at each given iteration of the decomposer algorithm that reduce the collective error (e.g., mean-square error) between the model and all of the input signals (rather than for one time-series dimension). It is observed that this modeling technique can provide a model having a high degree of modeling accuracy suitable to accurately isolate the residue data set representing the deterministic chao behavior and/or characteristic of the physiological system. In some embodiments, the operation has also been observed to improve performance (e.g., processing time, longer duration models, etc.).
[0114] A sparse approximation modeling operation, as a mathematical reconstruction of the wide-band phase-gradient biophysical-signal data signal, is configured to determine, in some embodiments, a linear combination of candidate terms that are iteratively selected to approximate a source signal that is, or derived from, the biophysical-signal data set 108. In some embodiments, the sparse-approximation signal decomposition algorithm generates a model as a function with a weighted sum of basis functions in which basis function terms are sequentially appends to an initially empty basis to approximate a target function while reducing the approximation error.
[0115] In some embodiments, the sparse-approximation signal decomposition algorithm is configured to select complex exponential candidate terms and deriving pairs of sine and cosine terms (each with its own weight coefficients) from each selected complex exponential candidate term to add to the model. In some embodiments, the operation has been observed to further improve modeling performance (e.g., processing time, longer duration models, etc.).
[0116] In some embodiments, the sparse-approximation signal decomposition algorithm is configured to evaluate the addition of a new set of candidate basis functions without use of a nested loop (e.g., employing a matrix multiplication operator that, e.g., includes a positive-definite matrix for any inner product operation, e.g., involving an orthogonal expansion coefficient to avoid having nest loops, thereby increasing the number of model terms). In some embodiments, certain sparse-approximation signal decomposition algorithms that employ nested loops can recursively grow in a N.sup.2 relationship with each added candidate functions, which can unduly restrict the number of candidate functions that a model can select. In some embodiments, the operation has been observed to further improve modeling performance (e.g., processing time, longer duration models, etc.).
[0117] In some embodiments, the sparse-approximation signal decomposition algorithm is configured to iteratively and recursively select candidate basis functions to add to the model until a stopping condition is reached (e.g., an assessed accuracy value reaches a pre-defined accuracy value (e.g., X %), the model reaches a maximum allowable number of candidates, and/or the model has included all available candidates).
[0118] As noted above, the residue remaining once the modelled signal that has been subtracted from the input signal contains none of the traditional landmarks of a conventional ECG trace. Of particular relevance when assessing a physiological system such as the cardiovascular system, and in particular its cardiac function, is any change in the structural properties of the heart (which may be induced or affected by pathology and/or aging) that can affect the heart's ability to respond to stimulation. For example, if the heart is less compliant due to remodeling, a decrease in myocyte membrane function, or a loss of cardiomyocytes, the mechanical function of the heart could be constrained and forced toward more periodic behavior.
[0119] In other embodiments, the sparse-approximation signal decomposition algorithm can employ a forward step and reverse step (e.g., a two steps forward, one step back approach, etc.) decomposition feature to improve accuracy. In another embodiment, the fast orthogonal search with first term reselection can be used, e.g., as described in McGaughey et al., Using the Fast Orthogonal Search with First Term Reselection to Find Subharmonic Terms in Spectral Analysis, Annals of Biomedical Eng., Vol. 31, issue 6, pp 741-751 (June 2003), which is incorporated by reference herein.
[0120] Indeed, the combination of these decomposition techniques can facilitate the selection of non-trivial phases of different frequencies in modeling the biophysical signal data set. In some embodiments, the modeling or analysis module 120 is configured to model the pre-processed biophysical signal data set 118 for over 6000 data points, e.g., for a duration of greater than 60 seconds at 1 kHz sampling.
[0121] In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.1 Hz and about 10.0 Hz at about 0.1 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.1 Hz and about 20.0 Hz at about 0.1 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.1 Hz and about 30.0 Hz at about 0.1 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.1 Hz and about 40.0 Hz at about 0.1 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.1 Hz and about 50.0 Hz at about 0.1 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.1 Hz and greater than about 50.0 Hz at about 0.1 Hz increments.
[0122] In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.01 Hz and about 10 Hz at about 0.01 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.01 Hz and about 20.00 Hz at about 0.01 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.01 Hz and about 30.00 Hz at about 0.01 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.01 Hz and about 40.00 Hz at about 0.01 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.01 Hz and about 50.00 Hz at about 0.01 Hz increments. In some embodiments, module 120 is configured to select candidate terms having frequency components ranging between about 0.01 Hz and greater than about 50.00 Hz at about 0.01 Hz increments.
[0123] Indeed, other decomposition algorithms that can provide model accuracy greater than about 99% can be used. As discussed in U.S. Publication No. 2013/0096394, which is incorporated by reference herein in its entirety, a sparse approximation operation comprises a set of operations, often iterative, to find a best matching projection of a data set (e.g., multi-dimensional data) onto candidate functions in a dictionary. Each dictionary can be a family of waveforms that is used to decompose the input data set. The candidate functions, in some embodiments, are linearly combined to form a sparse representation of the input data set. These operations can be numerical or analytical. In some embodiments, the mathematical reconstruction is based on evolvable mathematical models, symbolic regression, principal component analysis (PCA), matching pursuit, orthogonal matching pursuit, orthogonal search, linear models optimized using cyclical coordinate descent, projection pursuit, LASSO, fast orthogonal search, Sparse Karhunen-Loeve Transform, or combinations thereof. The recited examples are not exhaustive and other sparse approximation algorithms or methods may be used as well as any variations and/or combinations thereof.
[0124] Referring still to
[0125] In some embodiments, the assessment system 110 is configured to generate grid coordinates from the model. The values of the grid coordinates may be directly subtracted from the source signal (e.g., the down-sampled pre-processed biophysical-signal data set).
[0126] In some embodiments, the analysis of the plurality of biophysical signal data sets comprises a quasi-periodic analysis of the frequency components of the plurality of biophysical signal data sets.
[0127] In some embodiments, the analysis of the plurality of biophysical signal data sets comprises a chaotic analysis of the frequency components of the plurality of biophysical signal data sets.
[0128] In some embodiments, the analysis of the plurality of biophysical signal data sets comprises a phase analysis of the plurality of biophysical signal data sets.
[0129] The method 600 then includes generating at step 610 a three-dimensional alpha shape from the three-dimensional residue point cloud data set. In some embodiments, the alpha parameter (namely; the radius of the sphere used to define adjacency) is set to about 0.55. In other embodiments, the alpha parameter may take on a different value. In some embodiments, assessment system 110 generates a first subspace A as the built signal from analysis and a second subspace B as the generated residue data set 125. The assessment system 110 is configured to, in some embodiments, combine subspace A and subspace C into a single point cloud using trihull code and alpha hull to generate a solid model as the phase space volumetric object. In other embodiments, Delaunay triangulation, alpha shapes, ball pivoting, Mesh generation, Convex Hull triangulation, and the like, is used. Table 1 shows pseudocode that can be used to generate a solid model from a point cloud data set, expressed as normalizedResidue matrix.
TABLE-US-00001 TABLE 1 alphaRadius = 0.55; shp = alphaShape(normalizedResidue, alphaRadius); triHullSubspace = boundaryFacets(shp);
[0130] Per Table 1, an alpha shape operator is performed on the normalizedResidue matrix to generate an alpha shape object. A boundaryFacets operator then acts on the alpha shape object to return a triangulation of the alpha shape (e.g., a matrix representing the facets that make up the boundary of the alpha shape in which the facets represent edge segments in 2-D and triangles in 3-D).
[0131] The generated three-dimensional alpha shape can be outputted at step 612 in a report in any number of useful formats. Other operations maybe used, including those described in relation to
[0132] In some embodiments, method 600 then includes generating at step 614 fractionally-differentiated values to colorize, shade, gradate, and/or otherwise further differentiate aspects of the generated three-dimensional alpha shape to convey the information in a desired way. In some embodiments, the fractional derivative operation is based on Grunwald-Letnikov fractional derivative method. In some embodiments, the fractional derivative operation is based on Lubich's fractional linear multi-step method. In some embodiments, the fractional derivative operation is based on the fractional Adams-Moulton method. In some embodiments, the fractional derivative operation is based on the Riemann-Liouville fractional derivative method. In some embodiments, the fractional derivative operation is based on Riesz fractional derivative method. Other methods of performing a fractional derivative operation may be used.
[0133] In an embodiment, the system then colors the generated three-dimensional alpha shape by mapping each the triangular faces of the alpha shape to a color gradient, e.g., from blue to red, as an average of fractionally-differentiated values computed at three vertices of an analytical or numerical derivative of one of the input channels. Other embodiments may employ, in addition to or in substitution for the use of color/color gradients, non-color shading, gradation, and other techniques that can convey this information may be utilized and be within the scope of the present disclosure. Other report and reporting mechanisms maybe employed including those described in relation to
[0134] In some embodiments, various views of the volumetric object generated from the residue point cloud model/data set are captured for presentation, e.g., via a secure web portal, to a healthcare provider (e.g., a physician) to assist the healthcare provider in the assessment of presence or non-presence of disease or condition (e.g., presence or non-presence of significant coronary artery disease). In some embodiments, the parametric features are derived from the volumetric object generated from the residue point cloud model/data set and are assessed by a trained neural network classifier configured to assess for presence or non-presence of a disease state or other condition (e.g., significant coronary artery disease). In some embodiments, the features are presented alongside the results of a machine-generated predictions to assist in the physician in making a diagnosis.
[0135] In some embodiments, system 100a generates one or more images 126 of the volumetric object generated from the residue point cloud model/data set in which the vertices, face triangulations, and vertex colors are presented. In some embodiments, multiple views of the representation is generated and included in a report (e.g., per
[0136] In variants in which the presentation of the volumetric object generated from the residue point cloud model/data set may be supplemented and/or enhanced to provide additional utility, the parametric features of the volumetric object generated from the residue point cloud model/data set are analyzed in machine learning operations (e.g., image-based machine learning operations or feature-based machine learning operations, e.g., via predictor module 132) to determine the one or more coronary physiological parameters (e.g., to aid a healthcare provider in making a diagnosis of disease). In some embodiments, the assessment system 110 (e.g., the predictor module 132) is configured to determine a volume metric (e.g., alpha hull volume) of the phase space analysis data set/image. In some embodiments, the assessment system 110 is configured to determine a number of distinct bodies (e.g., distinct volumes) of the generated phase space data set/image. In some embodiments, the assessment system 110 is configured to assess a maximal color variation (e.g., color gradient) of the generated volumetric object generated from the residue point cloud model/data set.
[0137] In some embodiments, the volumetric object generated from the residue point cloud model/data set, or parameters derived therefrom, may be used as part of a machine learned classifier or predictor module 132 (e.g., that is either local and/or remote to the assessment system 110) to assist in the determination of the presence or absence of disease. The predictor module 132 can generate indicators 134 of presence or absence of disease (e.g., binary indicator of disease present and/or binary indicator of disease present in specific regions of the physiological region), which can be co-presented on the report 130 via the physician or clinician portal 128.
[0138] In some embodiments, the machine-learned classifier is instantiated on a machine-learned classifier selected from the group consisting of GoogLeNets, ResNets, ResNeXts, DenseNets, and DualPathNets, e.g., to train a machine-learned classifier to predict presence or absence of coronary artery disease (and/or significant coronary artery disease). In some embodiments, the machine-learned classifier is instantiated on an artificial intelligence platform such as, e.g., IBM Watson, Microsoft Azure, Google Cloud AI, Amazon AI, etc. to train a custom machine-learned classifier to predict presence or absence of coronary artery disease or condition (and/or significant coronary artery disease).
[0139] The machine-learned classifier, in some embodiments, uses deep learning methods to classify images into one or more positive classes and/or one more negative classes. In some embodiments, the deep learning methods are used to train both a CAD-positive class and a CAD-negative class in which a positive class, in some embodiments, is one in which the class can be defined as being part of a membership and a negative class is one in which the class can be defined as an exclusion from any of the positive class membership.
[0140] For training purposes, three-dimensional volumetric objects of a residue point-cloud model from an analysis of a physiological system may be processed in a coronary artery disease automated assessment pipeline. The pipeline may retrieve cardiovascular-related wide-band phase-gradient biophysical signal data sets associated with training and validation as well as verification and gating functions as may be desired.
[0141] A machine learning algorithm (e.g., meta-genetic algorithm), in some embodiments, is used to generate predictors linking aspects of the phase model (e.g., pool of features) across a population of patients representing both positive (i.e., have a disease or condition) and negative (i.e., do not have a disease or condition) cases to detect the presence of myocardial tissue associated with, e.g., significant coronary artery disease. In some embodiments, the performances of the candidate predictors are evaluated through a verification process against a previously unseen pool of patients. In some embodiments, the machine learning algorithm invokes a meta-genetic algorithm to automatically select a subset of features drawn from a large pool of features. This feature subset can then be used by an algorithm such as an adaptive boosting (AdaBoost) algorithm to generate predictors to diagnose significant coronary artery disease across a population of patients representing both positive and negative cases. The performances of the candidate predictors are determined through verification against a previously unseen pool of patients. A further description of the AdaBoost algorithm is provided in Freund, Yoav, and Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, European conference on computational learning theory, Springer, Berlin, Heidelberg (1995), which is incorporated by reference herein in its entirety.
[0142] In other embodiments, predictor module 132 is configured to predict the presence or non-presence of significant coronary artery disease or condition from the three-dimensional volumetric object by projecting the object at pre-defined or even user-selectable set number of views (e.g., six views) (e.g., a top view, a bottom view, a front view, a back view, a left view, and a right view). In some embodiments, each projected image is first converted to grayscale and scaled to a pre-defined image resolution (e.g., less than 200200 pixels). Other pixel count and image resolution values can be used. In some embodiments, the neural network classifier includes multiple hidden neurons (e.g., up to 15 hidden neurons) with leaky rectified linear activations. Dropout may be used between the hidden layer and the final output neuron to prevent overfitting. L1 and L2 regularization penalties may also be applied. A binary cross entropy may be used as a loss function. Optimization may be performed using the gradient-based Adam algorithm, among others.
[0143] Heat maps and contour plots, in some embodiments, are generated from the outputs of the neural network classifier on a given phase space analysis data set/image or from the computed phase space images themselves.
[0144] In some embodiments, a 44 moving window of white pixels (e.g., having a value of 1 in grayscale images) is swept over the entire image, with the neural network classifier being evaluated once for each window position and the output of the neural network being recorded. When a given pixel in the phase space analysis data set/image is covered by the moving window more than once (e.g., when the window is larger than a single pixel but moving one pixel at a time), each pixel in the heat map may have a value that is an average output of the neural network classifier when the corresponding pixel in the phase space analysis data set/image is covered by the window. Contour plots may be generated using the same data as the heat maps.
[0145]
[0146]
[0147] Example of heat maps and contour plots are further described in U.S. application Ser. No. 16/232,586, field on Dec. 26, 2018, title Method and System to Assess Disease Using Phase Space Tomography and Machine Learning, which is incorporated by reference.
[0148] Three-Dimensional Volumetric Object Interpretation Discussion
[0149] Without wishing to be bound to a particular theory, in one implementation of the volumetric object generated from the residue point cloud model/data set (e.g., in the assessment of chaotic or quasi-periodic behavior of the physiological system), CAD-positive data sets can be assumed to have a smaller set of disease etiologies (i.e., underlying causes) as compared to CAD-negative data sets. For example, chest pain as experienced by a CAD-negative patient can manifest from a larger set of etiologies. In contrast, chest pain as experienced by a CAD-positive patient may be more likely to be directly linked to, e.g., myocardial ischemia as induced by flow-limiting lesions associated with coronary artery disease. Indeed, the more restricted the possible available states observed in the chaotic behavior of the physiological system, the more likely the subject has an underlying disease or condition (associated with damaged tissue) that is responsible for the restriction, supporting a correlation to the geometric and topographic features observed in the phase space analysis data sets/images as disclosed herein.
[0150] In some embodiments, analysis of certain volumetric object generated from the residue point cloud model/data set may be represented visually with a repetitive set of distinct paths, or loops, in phase space (e.g., having two or more large distinct loops). CAD-positive data set, e.g., may have less fragmentation in the loops and high number of loops based on certain underlying analysis performed (e.g., based on quasi-periodic or chaotic analysis of the physiological system). CAD-positive data set are also expected to have orthogonal loops or at least have some angle between observed loops in such analysis. Such visual features can be observed in the plots of
[0151] To make the three-dimensional point-cloud of the residue point cloud/model data set, e.g., of
[0152]
[0153]
[0154]
[0155]
[0156] As shown in
[0157] Referring still to
[0158] Three-dimensional volumetric object of a residue point-cloud model (e.g., of quasi-periodic or chaos analysis), in some embodiments, can be expected to be represented visually with a repetitive set of distinct paths, or loops, in phase space (e.g., having two or more large distinct loops). One can also expect to observe that CAD-positive data set will have less fragmentation in the loops and high number of loops. One can also expect to observe that a CAD-positive data set will have orthogonal loops or at least have some angle between observed loops. Such visual features can be observed in
[0159] CAD-negative images (e.g.,
[0160] Indeed, a generated three-dimensional volumetric object of a residue model of a physiological system can be used to view the functional characteristic of that system.
Experimental Results
[0161] A two-stage study called Coronary Artery DiseaseLearning Algorithm Development (CADLAD) was implemented to support the development and testing of the machine-learned algorithms in connection with the present disclosure. In Stage 1 of the CADLAD study, paired human clinical biophysical data were used to guide the design and development of pre-processing, feature extraction, and machine learning phase of the development. That is, the collected clinical were split into three cohorts: training (50%), validation (25%), and verification (25%). Similar to the steps described above for processing signals from a patient for analysis, each signal was first pre-processed to clean and normalize the data. Following these processes, a set of features were extracted from the signals in which each set of features was paired with a representation of the true conditionfor example, the binary classification of the presence or absence of significant CAD. The final output of this stage was a fixed algorithm embodied within a measurement system.
[0162] In Stage 2 of the CADLAD study, the machine-learned algorithms were used to provide a determination of significant CAD against a pool of previously untested clinical data. The criteria for disease for the CADLAD study was established as defined in the American College of Cardiology (ACC) clinical guidelines; specifically, greater than 70% stenosis as determined by angiography or less than a 0.80 fractional-flow reserve (FFR) value as measured by flow wire.
[0163] In an aspect of the CADLAD study, an assessment system was developed that automatically and iteratively explores combines 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 sets were used as a comparator. Once candidate predictors had been developed, they were 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.
[0164] Results for predictors were computed on a test set of N=343 human subjects.
[0165] Modeling Results.
[0166] In another aspect of the study, experiments were conducted to quantify contributions of a resulting residue from physiological abnormalities, modeling error, or a combination of both. The study concluded that valuation information about the heart functionality is hidden in the residue. The basis, discussed herein and summarized again, is that an abnormality cannot be modeled (i.e., exhibiting predominantly deterministic chaos behavior and not quasi-periodic behavior), and thus would appear in the residue.
[0167]
[0168] In another aspect of the CADLAD study, the number of selected basis functions that collectively form the subspace model 122a were varied. In
[0169]
[0170] To further quantify this modeling noise, a study was conducted using a Fast Fourier Transform (FFT) technique or analysis to model the signal. It is generally understood that Fast Fourier Transform can be used to perfectly decompose and reconstruct a signal, but Fast Fourier Transform would model the noise as well.
[0171] While a portion of the PSD fluctuations may be attributed to variations in noise level within the signal, such portion was removed in the residue analysis through bias removal. Per the CADLA study, therefore, it is concluded that a sparse approximation algorithm can induce modeling noise (in the residue) that are mostly inbounded with the residues in phase space. In conjunction with FFT analysis, the model noise can be quantified.
Example Computing Device
[0172]
[0173] 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.
[0174] 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.
[0175] 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.
[0176] With reference to
[0177] Computing device 1700 may have additional features/functionality. For example, computing device 1700 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
[0178] Computing device 1700 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by the device 1700 and includes both volatile and non-volatile media, removable and non-removable media.
[0179] 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 1704, removable storage 1708, and non-removable storage 1710 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 1700. Any such computer storage media may be part of computing device 1700.
[0180] Computing device 1700 may contain communication connection(s) 1712 that allow the device to communicate with other devices. Computing device 1700 may also have input device(s) 1714 such as a keyboard, mouse, pen, voice input device, touch input device, etc., singularly or in combination. Output device(s) 2716 such as a display, speakers, printer, vibratory mechanism, etc. may also be included singularly or in combination. All these devices are well known in the art and need not be discussed at length here.
[0181] 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 Graphical Processing Units (GPUs), 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.
[0182] Although exemplary 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.
[0183] 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.
[0184] Further examples of various 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. 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. 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. Publication No. 2018/0249960, entitled Method and System for Wide-band Phase Gradient Signal Acquisition; U.S. application Ser. No. 16/232,801, filed on Dec. 26, 2018, 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. Publication No. 2019/0214137, filed on Dec. 26, 2018, entitled Method and System to Assess Disease Using Phase Space Tomography and Machine Learning; PCT Application No. PCT/IB32018/060709, entitled Method and System to Assess Disease Using Phase Space Tomography and Machine Learning; U.S. Publication No. 2019/0384757, entitled Methods and Systems to Quantify and Remove Asynchronous Noise in Biophysical Signals, filed Jun. 18, 2019; U.S. Publication No. 2019/0365265, entitled Method and System to Assess Pulmonary Hypertension Using Phase Space Tomography and Machine Learning; U.S. patent application Ser. No. ______, concurrently filed herewith, entitled Method and System for Automated Quantification of Signal Quality (having attorney docket no. 10321-036us1 and claiming priority to U.S. Patent Provisional Application No. 62/784,962); U.S. patent application Ser. No. ______, entitled Method and System to Configure and Use Neural Network To Assess Medical Disease (having attorney docket no. 10321-037pv1 and claiming priority to U.S. Patent Provisional Application No. 62/784,925); U.S. 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. application Ser. No. 15/653,431, entitled Discovering Genomes to Use in Machine Learning Techniques; U.S. patent application Ser. No. ______ (claiming priority to application having attorney docket no. 10321-041pv1), entitled Method and System to Assess Disease Using Dynamical Analysis of Cardiac and Photoplethysmographic Signals); U.S. patent application Ser. No. ______ (claiming priority to application having attorney docket no. 10321-040pv1), entitled Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals, each of which is incorporated by reference herein in its entirety.
[0185] 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.
[0186] 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.
[0187] 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.
[0188] Examples of other biophysical signals that may be analyzed in whole, or in part, using the exemplary 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.
[0189] The exemplary 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 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.