Patent Application
20230055617
The present disclosure generally relates to methods and systems for engineering features or parameters from biophysical signals for use in diagnostic applications; in particular, the engineering and use of photoplethysmographic waveform-based features for use in characterizing one or more physiological systems and their associated functions, activities, and abnormalities. The features or parameters may also be used for monitoring or tracking, controls of medical equipment, or to guide the treatment of a disease, medical condition, or an indication of either.
There are numerous methods and systems for assisting a healthcare professional in diagnosing disease. Some of these involve the use of invasive or minimally invasive techniques, radiation, exercise or stress, or pharmacological agents, sometimes in combination, with their attendant risks and other disadvantages.
Diastolic heart failure, a major cause of morbidity and mortality, is defined as symptoms of heart failure in a patient with preserved left ventricular function. It is characterized by a stiff left ventricle with decreased compliance and impaired relaxation leading to increased end-diastolic pressure in the left ventricle, which is measured through left heart catheterization. Current clinical standard of care for diagnosing pulmonary hypertension (PH), and for pulmonary arterial hypertension (PAH), in particular, involves a cardiac catheterization of the right side of the heart that directly measures the pressure in the pulmonary arteries. Coronary angiography is the current standard of care used to assess coronary arterial disease (CAD) as determined through the coronary lesions described by a treating physician. Non-invasive imaging systems such as magnetic resonance imaging and computed tomography require specialized facilities to acquire images of blood flow and arterial blockages of a patient that are reviewed by radiologists.
It is desirable to have a system that can assist healthcare professionals in the diagnosis of cardiac disease and various other diseases and conditions without the aforementioned disadvantages.
A clinical evaluation system and method are disclosed that facilitate the use of one or more PPG waveform-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. Photoplethysmographic waveforms may be acquired via a pulse oximeter or similar equipment that illuminates the skin and measures changes in light absorption at two or more distinct wavelengths.
The estimation or determined likelihood of the presence or non-presence of a disease, condition, or indication of either can supplant, augment, or replace other evaluation or measurement modalities for the assessment of a disease or medical condition. In some cases, a determination can take the form of a numerical score and related information.
For a given photoplethysmographic waveform (“PPG waveform”), the first derivative of the PPG waveform may be computed to generate a velocityplethysmogram (VPG) waveform (“VPG waveform”), and a second derivative of the PPG waveform may be computed to generate an accelerationplethysmogram (APG) waveform (“APG waveform”). PPG waveform-based features or parameters may include PPG waveform features or parameters, VPG waveform features or parameters, and/or APG waveform features or parameters. The PPG waveform-based features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions. In some embodiments, the waveforms may be analyzed in the frequency domain.
The exemplary system employs a hemodynamic delineator to identify fiducial points in the PPG waveform(s) and its corresponding VPG and APG waveforms. Waveform (“linear”) features that describe the PPG, VPG, and APG waveform characteristics in the time domain such as amplitude attributes of the fiducial points or duration/geometric attributes among the fiducial points may be determined solely from an acquired PPG signal data set (i.e., without information from other sources).
As used herein, the term “feature” (in the context of machine learning and pattern recognition and as used herein) generally refers to an individual measurable property or characteristic of a phenomenon being observed. A feature is defined by analysis and may be determined in groups in combination with other features from a common model or analytical framework.
As used herein, “metric” refers to an estimation or likelihood of the presence, non-presence, severity, and/or localization (where applicable) of one or more diseases, conditions, or indication(s) of either, in a physiological system or systems. Notably, the exemplified methods and systems can be used in certain embodiments described herein to acquire biophysical signals and/or to otherwise collect data from a patient and to evaluate those signals and/or data in signal processing and classifier operations to evaluate for a disease, condition, or indicator of one that can supplant, augment, or replace other evaluation modalities via one or more metrics. In some cases, a metric can take the form of a numerical score and related information.
In the context of cardiovascular and respiratory systems, examples of diseases and conditions to which such metrics can relate include, for example: (i) heart failure (e.g., left-side or right-side heart failure; heart failure with preserved ejection fraction (HFpEF)), (ii) coronary artery disease (CAD), (iii) various forms of pulmonary hypertension (PH) including without limitation pulmonary arterial hypertension (PAH), (iv) abnormal left ventricular ejection fraction (LVEF), and various other diseases or conditions. An example indicator of certain forms of heart failure is the presence or non-presence of elevated or abnormal left-ventricular end-diastolic pressure (LVEDP). An example indicator of certain forms of pulmonary hypertension is the presence or non-presence of elevated or abnormal mean pulmonary arterial pressure (mPAP).
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.
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.
Embodiments of the present invention may be better understood from the following detailed description when read in conjunction with the accompanying drawings. Such embodiments, which are for illustrative purposes only, depict novel and non-obvious aspects of the invention. The drawings include the following figures:
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.
While the present disclosure is directed to the practical assessment of biophysical signals, e.g., raw or pre-processed photoplethysmographic signals, biopotential/cardiac signals, etc., in the diagnosis, tracking, and treatment of cardiac-related pathologies and conditions, such assessment can be applied to the diagnosis, tracking, and treatment (including without limitation surgical, minimally invasive, lifestyle, nutritional, and/or pharmacologic treatment, etc.) of any pathologies or conditions in which a biophysical signal is involved in any relevant system of a living body. The assessment may be used in the controls of medical equipment or wearable devices or in monitoring applications (e.g., to report the PPG waveform-based features, parameters, or an intermediate output discussed herein)
The terms “subject” and “patient” as used herein are generally used interchangeably to refer to those who had undergone analysis performed by the exemplary systems and methods.
The term “cardiac signal” as used herein refers to one or more signals directly or indirectly associated with the structure, function, and/or activity of the cardiovascular system -including aspects of that signal’s electrical/electrochemical conduction - that, e.g., cause contraction of the myocardium. A cardiac signal may include, in some embodiments, biopotential signals or electrocardiographic signals, e.g., those acquired via an electrocardiogram (ECG), the cardiac and photoplethysmographic waveform or signal capture or recording instrument later described herein, or other modalities.
The term “biophysical signal” as used herein includes but is not limited to one or more cardiac signal(s), neurological signal(s), ballistocardiographic signal(s), and/or photoplethysmographic signal(s), but it also encompasses more broadly any physiological signal from which information may be obtained. Not intending to be limited by example, one may classify biophysical signals into types or categories that can include, for example, electrical (e.g., certain cardiac and neurological system-related signals that can be observed, identified, and/or quantified by techniques such as the measurement of voltage/potential (e.g., biopotential), impedance, resistivity, conductivity, current, etc. in various domains such as time and/or frequency), magnetic, electromagnetic, optical (e.g., signals that can be observed, identified and/or quantified by techniques such as reflectance, interferometry, spectroscopy, absorbance, transmissivity, visual observation, photoplethysmography, and the like), acoustic, chemical, mechanical (e.g., signals related to fluid flow, pressure, motion, vibration, displacement, strain), thermal, and electrochemical (e.g., signals that can be correlated to the presence of certain analytes, such as glucose). Biophysical signals may in some cases be described in the context of a physiological system (e.g., respiratory, circulatory (cardiovascular, pulmonary), nervous, lymphatic, endocrine, digestive, excretory, muscular, skeletal, renal/urinary/excretory, immune, integumentary/exocrine and reproductive systems), one or more organ system(s) (e.g., signals that may be unique to the heart and lungs as they work together), or in the context of tissue (e.g., muscle, fat, nerves, connective tissue, bone), cells, organelles, molecules (e.g., water, proteins, fats, carbohydrates, gases, free radicals, inorganic ions, minerals, acids, and other compounds, elements, and their subatomic components. Unless stated otherwise, the term “biophysical signal acquisition” generally refers to any passive or active means of acquiring a biophysical signal from a physiological system, such as a mammalian or non-mammalian organism. Passive and active biophysical signal acquisition generally refers to the observation of natural or induced electrical, magnetic, optical, and/or acoustics emittance of the body tissue. Non-limiting examples of passive and active biophysical signal acquisition means include, e.g., voltage/potential, current, magnetic, optical, acoustic, and other non-active ways of observing the natural emittance of the body tissue, and in some instances, inducing such emittance. Non-limiting examples of passive and active biophysical signal acquisition means include, e.g., ultrasound, radio waves, microwaves, infrared and/or visible light (e.g., for use in pulse oximetry or photoplethysmography), visible light, ultraviolet light, and other ways of actively interrogating the body tissue that does not involve ionizing energy or radiation (e.g., X-ray). An active biophysical signal acquisition may involve excitation-emission spectroscopy (including, for example, excitation-emission fluorescence). The active biophysical signal acquisition may also involve transmitting ionizing energy or radiation (e.g., X-ray) (also referred to as “ionizing biophysical signal”) to the body tissue. Passive and active biophysical signal acquisition means can be performed in conjunction with invasive procedures (e.g., via surgery or invasive radiologic intervention protocols) or non-invasively (e.g., via imaging, ablation, heart contraction regulation (e.g., via pacemakers), catheterization, etc.).
The term “photoplethysmographic signal” as used herein refers to one or more signals or waveforms acquired from optical sensors that correspond to measured changes in light absorption by oxygenated and deoxygenated hemoglobin, such as light having wavelengths in the red and infrared spectra. Photoplethysmographic signal(s), in some embodiments, include a raw signal(s) acquired via a pulse oximeter or a photoplethysmogram (PPG). In some embodiments, photoplethysmographic signal(s) are acquired from off-the-shelf, custom, and/or dedicated equipment or circuitries that are configured to acquire such signal waveforms for the purpose of monitoring health and/or diagnosing disease or abnormal conditions. The photoplethysmographic signal(s) typically include a red photoplethysmographic signal (e.g., an electromagnetic signal in the visible light spectrum most dominantly having a wavelength of approximately 625 to 740 nanometers) and an infrared photoplethysmographic signal (e.g., an electromagnetic signal extending from the nominal red edge of the visible spectrum up to about 1 mm), though other spectra such as near-infrared, blue and green may be used in different combinations, depending on the type and/or mode of PPG being employed.
The term “ballistocardiographic signal,” as used herein, refers to a signal or group of signals that generally reflect the flow of blood through the entire body that may be observed through vibration, acoustic, movement, or orientation. In some embodiments, ballistocardiographic signals are acquired by wearable devices, such as vibration, acoustic, movement, or orientation-based seismocardiogram (SCG) sensors, which can measure the body’s vibrations or orientation as recorded by sensors mounted close to the heart. Seismocardiogram sensors are generally used to acquire “seismocardiogram,” which is used interchangeably with the term “ballistocardiogram” herein. In other embodiments, ballistocardiographic signals may be acquired by external equipment, e.g., bed or surface-based equipment that measures phenomena such as a change in body weight as blood moves back and forth in the longitudinal direction between the head and feet. In such embodiments, the volume of blood in each location may change dynamically and be reflected in the weight measured at each location on the bed as well as the rate of change of that weight.
In addition, the methods and systems described in the various embodiments herein are not so limited and may be utilized in any context of another physiological system or systems, organs, tissue, cells, etc., of a living body. By way of example only, two biophysical signal types that may be useful in the cardiovascular context include cardiac/biopotential signals that may be acquired via conventional electrocardiogram (ECG/EKG) equipment, bipolar wide-band biopotential (cardiac) signals that may be acquired from other equipment such as those described herein, and signals that may be acquired by various plethysmographic techniques, such as, e.g., photoplethysmography. In another example, the two biophysical signal types can be further augmented by ballistocardiographic techniques.
The example analysis and classifiers described herein may be used to assist a healthcare provider in the diagnosis and/or treatment of cardiac- and cardiopulmonary-related pathologies and medical conditions, or an indicator of one. Examples include significant coronary artery disease (CAD), one or more forms of heart failure such as, e.g., heart failure with preserved ejection fraction (HFpEF), congestive heart failure, various forms of arrhythmia, valve failure, various forms of pulmonary hypertension, among various other disease and conditions disclosed herein.
In addition, there exist possible indicators of a disease or condition, such as an elevated or abnormal left ventricular end-diastolic pressure (LVEDP) value as it relates to some forms of heart failure, abnormal left ventricular ejection fraction (LVEF) values as they relate to some forms of heart failure or an elevated mean pulmonary arterial pressure (mPAP) value as it relates to pulmonary hypertension and/or pulmonary arterial hypertension. Indicators of the likelihood that such indicators are abnormal/elevated or normal, such as those provided by the example analysis and classifiers described herein, can help a healthcare provider assess or diagnose that the patient has or does not have a given disease or condition. In addition to these metrics associated with a disease state of condition, other measurements and factors may be employed by a healthcare professional in making a diagnosis, such as the results of a physical examination and/or other tests, the patient’s medical history, current medications, etc. The determination of the presence or non-presence of a disease state or medical condition can include the indication (or a metric of measure that is used in the diagnosis) for such disease.
In
In the
Plots 110a' and 110b' show examples of the first biophysical-signal data set 110a and the second biophysical-signal data set 110a, respectively. Specifically, Plot 110a' shows an example of an acquired photoplethysmographic or hemodynamic signal. In Plot 110a', the photoplethysmographic signal is a time series signal having a signal voltage potential as a function of time as acquired from two light sources (e.g., infrared and red-light source). Plot 110b' shows an example cardiac signal comprising a 3-channel potential time series plot. In some embodiments, the biophysical signal capture system 102 preferably acquires biophysical signals via non-invasive means or component(s). In alternative embodiments, invasive or minimally-invasively means or component(s) may be used to supplement or as substitutes for the non-invasive means (e.g., implanted pressure sensors, chemical sensors, accelerometers, and the like). In still further alternative embodiments, non-invasive and non-contact probes or sensors capable of collecting biophysical signals may be used to supplement or as substitutes for the non-invasive and/or invasive/minimally invasive means, in any combination (e.g., passive thermometers, scanners, cameras, x-ray, magnetic, or other means of non-contact or contact energy data collection system as discussed herein). Subsequent to signal acquisitions and recording, the biophysical signal capture system 102 then provides, e.g., sending over a wireless or wired communication system and/or a network, the acquired biophysical-signal data set 110 (or a data set derived or processed therefrom, e.g., filtered or pre-processed data) to a data repository 112 (e.g., a cloud-based storage area network) of the assessment system 103. In some embodiments, the acquired biophysical-signal data set 110 is sent directly to the assessment system 103 for analysis or is uploaded to a data repository 112 through a secure clinician’s portal.
Biophysical signal capture system 102 is configured with circuitries and computing hardware, software, firmware, middleware, etc., in some embodiments, to acquire, store, transmit, and optionally process both the captured biophysical signals to generate the biophysical-signal data set 110. An example biophysical signal capture system 102 and the acquired biophysical-signal set data 110 are described in U.S. Pat. No. 10,542,898, entitled “Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition,” or U.S. Pat. Publication No. 2018/0249960, entitled “Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition,” each of which is hereby incorporated by reference herein in its entirety.
In some embodiments, biophysical signal capture system 102 includes two or more signal acquisition components, including a first signal acquisition component (not shown) to acquire the first biophysical signals (e.g., photoplethysmographic signals) and includes a second signal acquisition component (not shown) to acquire the second biophysical signals (e.g., cardiac signals). In some embodiments, the electrical signals are acquired at a multi-kilohertz rate for a few minutes, e.g., between 1 kHz and 10 kHz. In other embodiments, the electrical signals are acquired between 10 kHz and 100 kHz. The hemodynamic signals may be acquired, e.g., between 100 Hz and 1 kHz.
Biophysical signal capture system 102 may include one or more other signal acquisition components (e.g., sensors such as mechano-acoustic, ballistographic, ballistocardiographic, etc.) for acquiring signals. In other embodiments of the signal capture system 102, a signal acquisition component comprises conventional electrocardiogram (ECG/EKG) equipment (e.g., Holter device, 12 lead ECG, etc.).
Assessment system 103 comprises, in some embodiments, the data repository 112 and an analytical engine or analyzer (not shown - see
Classifier module 116 (e.g., ML classifier module) may include transfer functions, look-up tables, models, or operators developed based on algorithms such as but not limited to decision trees, random forests, neural networks, linear models, Gaussian processes, nearest neighbor, SVMs, Naive Bayes, etc. In some embodiments, classifier module 116 may include models that are developed based on ML techniques described in U.S. Provisional Pat. Application no. 63/235,960, filed Aug. 23, 2021, entitled “Method and System to Non-Invasively Assess Elevated Left Ventricular End-Diastolic Pressure”; U.S. Pat. Publication No. 20190026430, entitled “Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions”; or U.S. Pat. Publication No. 20190026431, entitled “Discovering Genomes to Use in Machine Learning Techniques,” each of which is hereby incorporated by reference herein in its entirety.
As shown in
The electrical signals (e.g., corresponding to the second biophysical signal data set 110b) are collected using three orthogonally paired surface electrodes arranged across the patient’s chest and back along with a reference lead. The electrical signals are acquired, in some embodiments, using a low-pass anti-aliasing filter (e.g., ~ 2 kHz) at a multi-kilohertz rate (e.g., 8 thousand samples per second for each of the six channels) for a few minutes (e.g., 215 seconds). In alternative embodiments, the biophysical signals may be continuously/intermittently acquired for monitoring, and portions of the acquired signals are used for analysis. The hemodynamic signals (e.g., corresponding to the first biophysical signal data set 110a) are collected using a photoplethysmographic sensor placed on a finger. The photo-absorption of red light (e.g., any wavelengths between 600-750 nm) and infrared light (e.g., any wavelengths between 850-950 nm) are recorded, in some embodiments, at a rate of 500 samples per second over the same period. The biophysical signal capture system 102a may include a common mode drive that reduces common-mode environmental noise in the signal. The photoplethysmographic and cardiac signals were simultaneously acquired for each patient. Jitter (inter-modality jitter) in the data may be less than about 10 microseconds (µs). Jitter among the cardiac signal channels may be less than 10 microseconds, e.g., around ten femtoseconds (fs).
A signal data package containing the patient metadata and signal data may be compiled at the completion of the signal acquisition procedure. This data package may be encrypted before the biophysical signal capture system 102a transfers the package to the data repository 112. In some embodiments, the data package is transferred to the assessment system (e.g., 103). The transfer is initiated, in some embodiments, following the completion of the signal acquisition procedure without any user intervention. The data repository 112 is hosted, in some embodiments, on a cloud storage service that can provide secure, redundant, cloud-based storage for the patient’s data packages, e.g., Amazon Simple Storage Service (i.e., “Amazon S3”). The biophysical signal capture system 102a also provides an interface for the practitioner to receive notification of an improper signal acquisition to alert the practitioner to immediately acquire additional data from the patient.
Estimation of Presence of Disease State or Indicating Condition.
As stated above, one example in the cardiac context is the estimation of the presence of abnormal left-ventricular end-diastolic pressure (LVEDP) or mean pulmonary artery pressure (mPAP), significant coronary artery disease (CAD), abnormal left ventricular ejection fraction (LVEF), and one or more forms of pulmonary hypertension (PH), such as pulmonary arterial hypertension (PAH). Other pathologies or indicating conditions that may be estimated include, e.g., one or more forms of heart failure such as, e.g., heart failure with preserved ejection fraction (HFpEF), arrhythmia, congestive heart failure, valve failure, among various other diseases and medical conditions disclosed herein.
Method 300a further includes the step of retrieving (304) the data set and determining values of PPG waveform-based features or parameters that characterize waveform attributes such as amplitude, duration, and geometry of fiducial points in a PPG, VPG, and/or APG waveform. Example operations to determine the values of PPG waveform-based features or parameters are provided in relation to
Method 300a further includes the step of outputting (308) estimated value(s) for the presence of disease state or abnormal condition in a report (e.g., to be used diagnosis or treatment of the disease state, medical condition, or indication of either), e.g., as described in relation to
Diagnostics or Condition Monitoring or Tracking using PPG Waveform-based Features or Parameters.
Method 300b further includes determining (310) PPG waveform-based features or parameters from the acquired biophysical data set, e.g., as described in relation to
Method 300b further includes outputting (312) PPG waveform-based features or parameters (e.g., in a report for use in diagnostics or as signals for controls). As discussed herein, the PPG waveform-based features or parameters can provide a characterization of attributes such as amplitude, duration, and geometry of fiducial points in a PPG, VPG, or APG waveform. For monitoring and tracking, the output may be via a wearable device, a handheld device, or medical diagnostic equipment (e.g., pulse oximeter system, wearable health monitoring systems). In some embodiments, the output may be via a point of care monitoring, such as a mobile cart or cart trolley. In some embodiments, the outputs may be used in resuscitation systems, cardiac or pulmonary stress test equipment, pacemakers, or other equipment or application.
Table 1 shows an example set of 3 types of extractable waveform amplitude features based on their fiduciary points and their corresponding description to provide up to 45 features or parameters.
Tables 2A and 2B show a summarized set of 30 PPG waveform-based features (“Parameters”) for two PPG waveforms (from 2 PPG measurement channels) and an additional 60 features for the corresponding set of VPG and APG waveforms. In Tables 2A and 2B, 17 feature types (see “*” in Tables 2A and 2B) have been observed to have significant utility in the assessment of the presence or non-presence of at least one cardiac disease or condition -specifically, the determination of presence or non-presence of elevated LVEDP. It has also been observed through experimentation that the 25 feature types (see “**” in Tables 2A and 2B) have significant utility in the assessment of the presence or non-presence of coronary artery disease. The list of the specific features determined to have significant utility in the assessment of the presence or non-presence of abnormal or elevated LVEDP and the presence or non-presence of significant CAD is provided in Table 9A and Table 10A, respectively.
To perform the pre-processing operation (802), Module 400 may perform sub-signaling, down-sampling, high-frequency noise and powerline interference removal, and baseline removal operations. To remove the baseline, Module 40 may employ a high-pass filter (e.g., a high-pass filter with a cut-off frequency of 0.3 Hz). To remove high-frequency noise and powerline interference, Module 400 may employ a low-pass filter (e.g., a low-pass digital filter, e.g., with passband frequency of 30 Hz, a stopband frequency of 35 Hz, a passband ripper of 1 dB, and a stopband attenuation of 80 dB. Module 400 may also perform a smoothing operation (e.g., having a centered moving average of 6 points).
To generate the VPG and APG waveforms (804), Module 400 may (i) invert the PPG to generate an absorptive PPG signal, (ii) perform a first derivative operation of the PPG waveform to generate the VPG waveform, and (iii) perform a second derivative of the PPG waveform (or a first derivative of the VPG waveform) to generate the APG waveform. e.g., the adsorptive PPG signal). Any number of PPG, VPG, and/or AGP waveforms may be analyzed.
To determine (806) fiduciary landmarks in the PPG (absorptive), VPG, and APG waveforms, Module 400 employs a hemodynamic delineator that is configured to detect (i) landmarks or fiducial points such as pulse base, systolic peak, and diastolic peak landmarks in the PPG (absorptive) waveform and/or (ii) peak, minimum, and base landmarks in the VPG or APG waveforms.
Hemodynamic Delineator.
Photoplethysmographic (PPG) Waveform Segmentation.
PPG Pulse Base (906). Referring to
The pulse bases (906) may be used to isolate and create a 20-second segment of the signal containing a single cycle centered at the second 10 and padded with the base onset and offset. The PPG pulse base (906) may be detected and used for the detection of other landmarks.
Systolic peak (902):
Diastolic peak (904):
To determine the VPG min (914), the PPG signal is segmented using the indexes of PPG systolic peak (e.g., 902) at cycle n and the consecutive PPG pulse base (e.g., 906) at cycle n + 1. The segmented PPG is smoothened, e.g., with a 20-datapoint Gaussian-weighted moving average filter. A VPG signal is derived from the smoothed PPG, and the peaks for the inverted VPG are detected using a peak finder operator. The detected peaks are then filtered by applying a time constraint that the VPG min should occur at the first 30% of indexes of the segmented VPG. A maximum filter is applied to the original VPG (non-smoothed) that searches for local minima around the detected VPG min in the smoothed VPG.
Velocityplethysmogram (VPG) Waveform Segmentation.
VPG Peak (912). To find the VPG peak landmarks (912), Method 1032 includes segmenting (1038) indexes from a PPG signal corresponding to a monotonically increasing segment in the PPG signal, also referred to as PPG raise-segments. The PPG raise-segments for cycle n may be identified as the data points between the PPG pulse base at cycle n and a consecutive PPG systolic peak at cycle n+1. Method 1032 may detect (1040) VPG peaks using a peak finder operator, e.g., configured with a minimum pulse width of 25% of the median of the PPG raise-duration (the time in ms associated with the PPG raise-segment) across the VPG signal. Method 1032 may filter (1042) the detected peaks using the criteria: (i) the minimum peak width (at the half-prominence) be less than the VPG minimum pulse width and (ii) VPG peak values be less than ten scaled MAD away from the median of the detected peaks. Method 1032 may apply (1042) a maximum filter to identify the maximum value of the detected peaks within the PPG raised segment as the VPG peak for the corresponding cycle.
VPG min. To determine the VPG min (912), Method 1034 includes segmenting (1044) the PPG signal using the indexes of the PPG systolic peak (e.g., 902) at cycle n and the consecutive PPG pulse base at cycle n+1. Method 1034 includes smoothing (1046) the segmented PPG, e.g., with a 20-datapoint Gaussian-weighted moving average filter. Method 1034 may derive (1048) an inverted VPG signal from the smoothed PPG and detect (1048) peaks of the inverted VPG. To detect the peaks, Method 1034 may filter for detected peaks having a time constraint that the VPG min occurs at the first 30% of indexes of the segmented VPG. Method 1034 may apply (1050) a maximum filter to the original VPG (non-smoothed) to search for local minima around the detected VPG min in the smoothed VPG.
VPG base (916). To determine the VPG base (916), Method 1036 includes correcting (1052) a baseline from the VPG signal that may occur during the numerical derivative operation performed on the PPG signal. The baseline correction (1052) may be performed by percentile filtering the VPG waveform to identify the data points with the values between the 20% and 50% percentile of the VPG waveform and then subtracting the VPF waveform from the mean of the percentile filtered VPG. Then, Method 1036 may complete the baseline correction 1052 by segmenting the baseline-corrected VPG using the indexes of VPG min (914) at cycle n and a consecutive VPG peak at cycle n+1. Once the baseline correction is completed, Method 1036 may determine (1054) the VPG bases through zero-crossing the VPG where VPG bases less than four-scaled median absolute deviations (MAD) away from the median of the detected bases may be removed.
Accelerationplethysmogram (APG) Waveform Segmentation. As noted above,
APG peak (924). To determine the APG peak (e.g., 924), Method 1062 includes segmenting (1068) the PPG waveform using the indexes PPG pulse base (e.g., 906) at cycle n and the consecutive PPG systolic peak (e.g., 902) at cycle n+1. Method 1062 then includes detecting (1070) the AGP peaks (924). Any peak detection algorithm may be used. An example is the “findpeak” function manufactured by MathWorks in Matlab R2019a. A maximum filter is applied (1072) so that the maximum value of the detected peaks within the segment is found, and peaks with the value of more than six scaled MAD away from the median of the detected may be removed.
APG min (926). To determine the APG min (e.g., 926), Method 1064 includes segmenting (1074) the PPG signal using the indexes of the PPG pulse base (e.g., 906) at cycle n and the consecutive PPG systolic peak (e.g., 902) at cycle n+1. Method 1064 includes smoothing (1076) the segmented PPG. Any smoothing operator may be used. An example is a smooth-data operator manufactured by MathWorks in Matlab R2019a. The smoothing operator may employ a 20-datapoint Gaussian-weighted moving average filter. Method 1064 may derive (1078) an inverted APG signal from the smoothed PPG and detect (1078) peaks of the inverted APG. To detect the peaks, Method 1064 may filter for detected peaks having a time constraint that the APG min occurs at the first 75% of indexes of the segmented APG. Method 1064 then applies (1080) minimum filter to find the minimum value in the original APG (non-smoothed) by searching around the detected minimum in the smoothed APG. APG min with the value of more than six scaled MAD away from the median of the detected may be removed.
APG base (928). To determine the APG base (928), Method 1066 includes correcting (1082) a baseline from the APG signal that may occur during the numerical derivative operation performed on the PPG signal. The baseline correction (1082) may be performed by percentile filtering the APG waveform to identify the data points with the values between the 25% and 75% percentile of the APG waveform and then subtracting the APG waveform from the mean of the percentile filtered APG. Then, Method 1066 may complete the baseline correction 1082 by segmenting the baseline-corrected APG using the indexes of APG min (926) at cycle n and a consecutive APG peak at cycle n+1. Once the baseline correction is completed, Method 1066 may determine (1084) the APG bases through zero-crossing the VPG where VPG bases less than four-scaled median absolute deviations (MAD) away from the median of the detected bases may be removed.
Table 3 shows an example set of 12 extractable waveform duration features and their corresponding description to provide up to 36 features or parameters.
Table 4 shows a set of 12 PPG waveform-based features for two PPG waveforms (from 2 PPG measurement channels) and an additional 24 features for the corresponding set of VPG and APG waveforms. In Table 4, 3 features (see “*” in Table 4) have been observed to have significant utility in the assessment of the presence or non-presence of at least one cardiac disease or condition - specifically, the determination of the presence or non-presence of elevated LVEDP. It has also been observed through experimentation that 3 feature types (see “**” in Table 4) have significant utility in the assessment of the presence or non-presence of coronary artery disease. The list of the specific features determined to have significant utility in the assessment of the presence or non-presence of abnormal or elevated LVEDP and the presence or non-presence of significant CAD is provided in Table 9B and Table 10B, respectively.
In window 1104 of
A corresponding set of intra-beat duration features can be defined between peaks, minimums, and base landmarks of a VPG waveform and an APG waveform (shown in Table 5 as peak-to-base duration (“baseDist_left”), peak-to-minimum duration (“minDist_left”), and minimum-to-peak duration (“peakDist_left”) for VPG and APG waveforms.
Table 5 shows an example set of 4 types of extractable waveform topologic features and their corresponding description to provide up to 12 features or parameters.
Table 6 shows a set of 4 PPG waveform-based features for a PPG waveform and an additional 8 features for the corresponding set of VPG and APG waveforms. In Table 6, all 12 features (see “*” in Table 6) have been observed to have significant utility in the assessment of the presence or non-presence of at least one cardiac disease or condition - specifically, the determination of presence or non-presence of elevated LVEDP. It has also been observed through experimentation that 6 feature types (see “**” in Table 4) have significant utility in the assessment of the presence or non-presence of coronary artery disease. The list of the specific features determined to have significant utility in the assessment of the presence or non-presence of abnormal or elevated LVEDP and the presence or non-presence of significant CAD is provided in Table 9C and Table 10C, respectively.
In the waveform associated with 1200a, the first triangle 1212 is defined among the average systolic peak 1202, the average pulse base 1208, and the average nearby pulse base 1210. In the waveform associated with 1200b, the second triangle 1214 is defined among the average systolic peak 1202, the average nearby systolic peak 1204, and the average pulse base 1208. In the waveform associated with 1200c, the third triangle 1216 is defined among the average systolic peak 1202, the average diastolic peak 1206, and the average pulse base 1208.
For each of the triangles (e.g., 1212, 1214, 1216), the angles (shown as 1218a, 1218b, 1218c) and the area of the respective triangle may be determined.
Oxygen saturation level (SpO2) is the measure of oxygen level in the blood given by Equation 1.
In Equation 1, HbO2 and Hb are oxyhemoglobin and deoxyhemoglobin values in the blood, respectively. The ratio in Equation 1 may be acquired using a pulse oximeter by extracting the AC and DC components of two photoplethysmographic waveforms. Oxygen saturation level may be calculated per Equation 2, where K is a constant proportional factor and R is defined per Equation 3.
It has also been observed through experimentation that 8 feature types (see “**” in Table 4) have significant utility in the assessment of the presence or non-presence of coronary artery disease. The list of the specific features determined to have significant utility in the assessment of the presence or non-presence of significant CAD is provided in Table 10D.
Other SpO2-associated features may be generated, including, for example, per the description provided in Cho et al., “A preliminary study on photoplethysmogram (PPG) signal analysis for reduction of motion artifact in frequency domain,” 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, pp. 28-33 (2012).
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 conditions, 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.
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
Within the feature development phases, features were developed, including the PPG waveform-based features or parameters, 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.
The univariate feature selection assessments evaluated many scenarios, each defined by a negative and a positive dataset pair using a t-test, mutual information, and AUC-ROC evaluation. The t-test is a statistical test that can determine if there is a difference between two sample means from two populations with unknown variances. Here, the t-tests were conducted against a null hypothesis that there is no difference between the means of the feature in these groups, e.g., normal LVEDP vs. elevated (for LVEDP algorithm development); CAD- vs. CAD+ (for CAD algorithm development). A small p-value (e.g., ≤ 0.05) indicates strong evidence against the null hypothesis.
Mutual information (MI) operations were conducted to assess the dependence of elevated or abnormal LVEDP or significant coronary artery disease on certain features. An MI score greater than one indicates a higher dependency between the variables being evaluated. MI scores less than one indicates a lower dependency of such variables, and an MI score of zero indicates no such dependency.
A receiver operating characteristic curve, or ROC curve, illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The ROC curve may be created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. AUC-ROC quantifies the area under a receiver operating characteristic (ROC) curve - the larger this area, the more diagnostically useful the model is. The ROC, and AUC-ROC, value is considered statistically significant when the bottom end of the 95% confidence interval is greater than 0.50.
Table 8 shows an example list of the negative and the positive dataset pair used in the univariate feature selection assessments. Specifically, Table 8 shows positive datasets being defined as having an LVEDP measurement greater than 20 mmHg or 25 mmHg, and negative datasets were defined as having an LVEDP measurement less than 12 mmHg or belonging to a subject group determined to have normal LVEDP readings.
Tables 9A, 9B, 9C each shows a list of PPG waveform-based features having been determined to have utility in estimating the presence and non-presence of elevated LVEDP in an algorithm executing in a clinical evaluation system. The features of Tables 9A, 9B, and 9C and corresponding classifiers have been validated to have clinical performance comparable to the gold standard invasive method to measure elevated LVEDP.
Tables 10A, 10B, 10C, and 10D each shows a list of PPG waveform-based features having been determined to have utility in estimating the presence and non-presence of significant CAD in an algorithm executing in a clinical evaluation system. The features of Tables 10A, 10B, 10C, and 10D and corresponding classifiers have been validated to have clinical performance comparable to the gold standard invasive method to measure significant CAD.
The determination that certain PPG waveform-based features have clinical utility in estimating the presence and non-presence of elevated LVEDP or the presence and non-presence of significant CAD provides a basis for the use of these PPG waveform-based features or parameters, as well as other features described herein, in estimating for the presence or non-presence and/or severity and/or localization of other diseases, medical condition, or an indication of either particularly, though not limited to, heart disease or conditions described herein.
The experimental results further indicate that intermediary data or parameters of PPG waveform-based features also have clinical utility in diagnostics as well as treatment, controls, monitoring, and tracking applications.
In various embodiments, different versions of the clinical evaluation system 1400 may implement the assessment system 103 (
In
The base system 1404 can provide a foundation of functions and instructions upon which each add-on module 1402 (which includes the disease-specific algorithm) then interfaces to assess for the pathology or indicating condition. The base system 1404 as shown in the example of
Data repository 112a, which can be cloud-based, stores data from the signal capture system 102 (shown as 102b). Biophysical signal capture system 102b, in some embodiments, is a reusable device designed as a single unit with a seven-channel lead set and photoplethysmogram (PPG) sensor securely attached (i.e., not removable). Signal capture system 102b, together with its hardware, firmware, and software, provides a user interface to collect patient-specific metadata entered therein (e.g., name, gender, date of birth, medical record number, height, and weight, etc.) to synchronously acquire the patient’s electrical and hemodynamic signals. The signal capture system 102b may securely transmit the metadata and signal data as a single data package directly to the cloud-based data repository. The data repository 112a, in some embodiments, is a secure cloud-based database configured to accept and store the patient-specific data package and allow for its retrieval by the analytical engines or analyzer 1406 or 1414.
Base analytical engine or analyzer 1406 is a secure cloud-based processing tool that may perform quality assessments of the acquired signals (performed via “SQA” module 1416), the results of which can be communicated to the user at the point of care. The base analytical engine or analyzer 1406 may also perform pre-processing (shown via pre-processing module 1418) of the acquired biophysical signals (e.g., 110 - see
Add-on module 1402 includes a second part 1414 (also referred to herein as the analytical engine (AE) or analyzer 1414 and shown as “AE add-on module” 1414) that operates with the base analytical engine (AE) or analyzer 1406. Analytical engine (AE) or analyzer 1414 can include the main function loop of a given disease-specific algorithm, e.g., the feature computation module 1420, the classifier model 1424 (shown as “Ensemble” module 1424), and the outlier assessment and rejection module 1424 (shown as “Outlier Detection” module 1424). In certain modular configurations, the analytical engines or analyzers (e.g., 1406 and 1414) may be implemented in a single analytical engine module.
The main function loop can include instructions to (i) validate the executing environment to ensure all required environment variables values are present and (ii) execute an analysis pipeline that analyzes a new signal capture data file comprising the acquired biophysical signals to calculate the patient’s score using the disease-specific algorithm. To execute the analysis pipeline, AE add-on module 1414 can include and execute instructions for the various feature modules 114 and classifier module 116 as described in relation to
The clinical evaluation system 1400 can manage the data within and across components using the web-service DTAPIs 1408 (also may be referred to as HCPP web services in some embodiments). DTAPIs 1408 may be used to retrieve acquired biophysical data sets from and to store signal quality analysis results to the data repository 112a. DTAPIs 1408 may also be invoked to retrieve and provide the stored biophysical data files to the analytical engines or analyzers (e.g., 1406, 1414), and the results of the analytical engine’s analysis of the patient signals may be transferred using DTAPI 1408 to the report database 1410. DTAPIs 1408 may also be used, upon a request by a healthcare professional, to retrieve a given patient data set to the web portal module 1413, which may present a report to the healthcare practitioner for review and interpretation in a secure web-accessible interface.
Clinical evaluation system 1400 includes one or more feature libraries 1426 that store the PPG waveform-based features 120 and various other features of the feature modules 122. The feature libraries 1426 may be a part of the add-on modules 1402 (as shown in
Further details of the modularity of modules and various configurations are provided in U.S. Provisional Pat. Application no. 63/235,960, filed Aug. 19, 2021, entitled “Modular Disease Assessment System,” which is hereby incorporated by reference herein in its entirety.
Signal quality assessment/rejection (1430). Referring to
The base analytical engine or analyzer 1406 performs two sets of assessments for signal quality, one for the electrical signals and one for the hemodynamic signals. The electrical signal assessment (1430) confirms that the electrical signals are of sufficient length, that there is a lack of high-frequency noise (e.g., above 170 Hz), and that there is no power line noise from the environment. The hemodynamic signal assessment (1430) confirms that the percentage of outliers in the hemodynamic data set is below a pre-defined threshold and that the percentage and maximum duration that the signals of the hemodynamic data set are railed or saturated is below a pre-defined threshold.
Feature Value Computation (1432). The AE add-on module 1414 performs feature extraction and computation to calculate feature output values. In the example of the LVEDP algorithm, the AE add-on module 1414 determines, in some embodiments, a total of 446 feature outputs belonging to 18 different feature families (e.g., generated in modules 120 and 122), including the PPG waveform-based features (e.g., generated in module 120). For the CAD algorithm, an example implementation of the AE add-on module 1214 determines a set of features, including 456 features corresponding to the same 18 feature families.
Additional descriptions of the various features, including those used in the LVEDP algorithm and other features and their feature families, are described in U.S. Provisional Pat. Application No. 63/235,960, filed Aug. 23, 2021, entitled “Method and System to Non-Invasively Assess Elevated Left Ventricular End-Diastolic Pressure”; U.S. Provisional Pat. Application No. 63/236,072, filed Aug. 23, 2021, entitled “Methods and Systems for Engineering Visual Features From Biophysical Signals for Use in Characterizing Physiological Systems”; U.S. Provisional Pat. Application No. 63/235,963, filed Aug. 23, 2021, entitled “Methods and Systems for Engineering Power Spectral Features From Biophysical Signals for Use in Characterizing Physiological Systems”; U.S. Provisional Pat. Application No. 63/235,966, filed Aug. 23, 2021, entitled “Method and System for Engineering Rate-Related Features_From Biophysical Signals for Use in Characterizing Physiological Systems”; U.S. Provisional Pat. Application No. 63/235,968, filed Aug. 23, 2021, entitled “Methods and Systems for Engineering Wavelet-Based Features From Biophysical Signals for Use in Characterizing Physiological Systems”; U.S. Provisional Pat. Application No. 63/130,324, titled “Method and System to Assess Disease Using Cycle Variability Analysis of Cardiac and Photoplethysmographic Signals”; U.S. Provisional Pat. Application No. 63/236,193, filed Aug. 23, 2021, entitled “Methods and Systems for Engineering Cardiac Waveform Features From Biophysical Signals for Use in Characterizing Physiological Systems”; U.S. Provisional Pat. Application No. 63/235,974, filed Aug. 23, 2021, entitled “Methods and Systems for Engineering Conduction Deviation Features From Biophysical Signals for Use in Characterizing Physiological Systems,” each of which is hereby incorporated by reference herein in its entirety.
Classifier Output Computation (1434). The AE add-on module 1414 then uses the calculated feature outputs in classifier models (e.g., machine-learned classifier models) to generate a set of model scores. The AE add-on module 1414 joins the set of model scores in an ensemble of the constituent models, which, in some embodiments, averages the output of the classifier models as shown in Equation 4 in the example of the LVEDP algorithm.
In some embodiments, classifier models may include models that are developed based on ML techniques described in U.S. Patent Publication No. 20190026430, entitled “Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions”; or U.S. Patent Publication No. 20190026431, entitled “Discovering Genomes to Use in Machine Learning Techniques,” each of which is hereby incorporated by reference herein in its entirety.
In the example of the LVEDP algorithm, thirteen (13) machine-learned classifier models are each calculated using the calculated feature outputs. The 13 classifier models include four ElasticNet machine-learned classifier models, four RandomForestClassifier machine-learned classifier models, and five extreme gradient boosting (XGB) classifier models. In some embodiments, the patient’s metadata information, such as age, gender, and BMI value, may be used. The output of the ensemble estimation may be a continuous score. The score may be shifted to a threshold value of zero by subtracting the threshold value for presentation within the web portal. The threshold value may be selected as a trade-off between sensitivity and specificity. The threshold may be defined within the algorithm and used as the determination point for test positive (e.g., “Likely Elevated LVEDP”) and test negative (e.g., “Not Likely Elevated LVEDP”) conditions.
In some embodiments, the analytical engine or analyzer can fuse the set of model scores with a body mass index-based adjustment or an adjustment based on age or gender. For example, the analytical engine or analyzer can average the model estimation with a sigmoid function of the patient BMI having the form sigmoid(x) =
Physician Portal Visualization (1436). The patient’s report may include a visualization 1436 of the acquired patient data and signals and the results of the disease analyses. The analyses are presented, in some embodiments, in multiple views in the report. In the example shown in
The healthcare portal may list a report for a patient if a given patient’s acquired signal data set meets the signal quality standard. The report may indicate a disease-specific result (e.g., elevated LVEDP) being available if the signal analysis could be performed. The patient’s estimated score (shown via visual elements 118a, 118b, 118c) for the disease-specific analysis may be interpreted relative to an established threshold.
In the score summary section 1440 shown in the example of
The threshold section 1442 shows reported statistics of the threshold as provided to a validation population that defines the sensitivity and specificity for the estimation of the patient score (e.g., 118). The threshold is the same for every test regardless of the individual patient’s score (e.g., 118), meaning that every score, positive or negative, may be interpreted for accuracy in view of the provided sensitivity and specificity information. The score may change for a given disease-specific analysis as well with the updating of the clinical evaluation.
The frequency distribution section 1444 illustrates the distribution of all patients in two validation populations (e.g., (i) a non-elevated population to indicate the likelihood of a false positive estimation and (ii) an elevated population to indicate a likelihood of a false negative estimation). The graphs (1446, 1448) are presented as smooth histograms to provide context for interpreting the patient’s score 118 (e.g., 118b, 118c) relative to the test performance validation population patients.
The frequency distribution section 1440 includes a first graph 1446 (shown as “Non-Elevated LVEDP Population” 1446) that shows the score (118b), indicating the likelihood of the non-presence of the disease, condition, or indication, within a distribution of a validation population having non-presence of that disease, condition, or indication and a second graph 1448 (shown as “Elevated LVEDP Population” 1448) that shows the score (118c), indicates the likelihood of the presence of the disease, condition, or indication, within a distribution of validation population having the presence of that disease, condition, or indication. In the example of the assessment of elevated LVDEP, the first graph 1446 shows a non-elevated LVEDP distribution of the validation population that identifies the true negative (TN) and false positive (FP) areas. The second graph 1448 shows an elevated LVEDP distribution of the validation population that identifies the false negative (TN) and true positive (FP) areas.
The frequency distribution section 1440 also includes interpretative text of the patient’s score relative to other patients in a validation population group (as a percentage). In this example, the patient has an LVEDP score of -0.08, which is located to the left side of the LVEDP threshold, indicating that the patient has “Not Likely Elevated LVEDP.”
The report may be presented in the healthcare portal, e.g., to be used by a physician or healthcare provider in their diagnosis for indications of left-heart failure. The indications include, in some embodiments, a probability or a severity score for the presence of a disease, medical condition, or an indication of either.
Outlier Assessment and Rejection Detection (1438). Following the AE add-on module 1414 computing the feature value outputs (in process 1432) and prior to their application to the classifier models (in process 1434), the AE add-on module 1414 is configured in some embodiments to perform outlier analysis (shown in process 1438) of the feature value outputs. Outlier analysis evaluation process 1438 executes a machine-learned outlier detection module (ODM), in some embodiments, to identify and exclude anomalous acquired biophysical signals by identifying and excluding anomalous feature output values in reference to the feature values generated from the validation and training data. The outlier detection module assesses for outliers that present themselves within sparse clusters at isolated regions that are out of distribution from the rest of the observations. Process 1438 can reduce the risk that outlier signals are inappropriately applied to the classifier models and produce inaccurate evaluations to be viewed by the patient or healthcare provider. The accuracy of the outlier module has been verified using hold-out validation sets in which the ODM is able to identify all the labeled outliers in a test set with the acceptable outlier detection rate (ODR) generalization.
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 PPG waveform-based features discussed herein may ultimately 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, medical conditions, or indication of either, 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 analysis, including the PPG waveform-based features, can be used in the diagnosis and treatment of other cardiac-related pathologies and indicating conditions as well as neurological-related pathologies and indicating conditions, such assessment can be applied to the diagnosis and treatment (including, surgical, minimally invasive, and/or pharmacologic treatment) of any pathologies or indicating 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, medical conditions, or indicating 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 indicating 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, indicating conditions and/or diseases. Non-limiting examples of neurological-related diseases, pathologies or indicating 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, indicating 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/indicating conditions and vision-related diseases/indicating conditions.
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.
Further examples of processing that may be used with the exemplified method and system disclosed herein are described in: U.S. Pat. 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. Pat. 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. WO2017/033164; WO2017/221221; WO2019/130272; WO2018/158749; WO2019/077414; WO2019/130273; WO2019/244043; WO2020/136569; WO2019/234587; WO2020/136570; WO2020/136571; U.S. Pat. Application Nos. 16/831,264; 16/831,380; 17/132869; PCT Application Nos. PCT/IB2020/052889; PCT/IB2020/052890, each of which is hereby incorporated by reference herein in its entirety.
The following patents, applications, and publications as listed below and throughout this document are hereby incorporated by reference in their entirety herein.
J. M. Cho, Y. K. Sung, K. W. Shin, D. J. Jung, Y. S. Kim and N. H. Kim, “A preliminary study on photoplethysmogram (PPG) signal analysis for reduction of motion artifact in frequency domain,” 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, 2012, pp. 28-33, doi: 10.1109/IECBES.2012.6498141.
This US application claims priority to, and the benefit of, U.S. Provisional Pat. Application No. 63/235,971, filed Aug. 23, 2021, entitled “Methods and Systems for Engineering Photoplethysmographic-Waveform Features From Biophysical Signals for Use in Characterizing Physiological Systems,” which is incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63235971 | Aug 2021 | US |
