Method and system for automated quantification of signal quality

Abstract

To provide a favorable method and system for automated quantification of signal quality.SOLUTION: Systems and methods for the quantification of the quality of an acquired signal are provided for assessment and for gating the acquired signal for subsequent analysis. A signal is acquired, and a determination is made in real time if there is a problem with the acquisition (e.g., if the acquired signal is acceptable or unacceptable, and if the acquired signal is of sufficient quality for subsequent assessment). If there is a problem, output is provided via the systems and methods described herein to indicate that signal acquisition needs to be performed again (e.g., if the acquired signal is unacceptable, rejecting the acquired signal and acquiring a new signal).SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] Related Applications This international PCT application claims the priority and benefit of U.S. Provisional Patent Application No. 62 / 784,962, entitled "Method and System for Automated Quantification of Signal Quality," filed on December 26, 2018, the entire disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure generally relates to non-invasive methods and systems for characterizing the cardiovascular and other physiological systems. More specifically, in one aspect, the present disclosure relates to the quality assessment of acquired biophysical signals (e.g., cardiac signals, brain / neurological signals, signals related to other biological systems, etc.) and the gating of signals acquired for analysis.

Background Art

[0003] Ischemic heart disease, also known as myocardial ischemia or cardiac ischemia, is a disease or group of diseases typically characterized by a reduction in blood supply to the myocardium, usually caused by coronary artery disease (CAD). CAD typically occurs when the inner lining of the coronary arteries that supply blood to the myocardium develops atherosclerosis (the inner lining hardens or becomes stiff, plaques accumulate there, and it is often accompanied by abnormal inflammation). Over time, CAD can weaken the myocardium and cause, for example, angina, myocardial infarction (heart attack), heart failure, arrhythmia, etc. Arrhythmia is an abnormal heart rhythm and can include some change from the normal electrical conduction sequence of the heart and, in some cases, can lead to cardiac arrest.

[0004] The evaluation of CAD can be complex, and many techniques and tools are used to assess the presence and severity of symptoms. In the case of electrocardiogram testing, which is a branch of cardiology that analyzes the electrical activity of the heart to obtain information about the heart's structure and function, severe ischemic heart disease can alter the ventricular conduction properties of the myocardium within the perfusion bed located downstream of the stenosis or occlusion of the coronary artery. This condition can manifest at various locations in the heart and at various stages of severity, making accurate diagnosis difficult. Furthermore, the electrical conduction properties of the myocardium can vary from person to person, and other factors such as measurement variations related to the placement of measurement probes and parasitic losses related to such probes and their associated components can also affect the biophysical signals obtained during electrophysiological testing of the heart. Additionally, when the conduction properties of the myocardium are obtained as relatively long cardiac phase gradient signals, they may exhibit complex non-linear variations that cannot be efficiently obtained using conventional modeling techniques.

[0005] Regardless of whether the acquired biophysical signal is a cardiac signal, a neural signal, or some other biophysical signal, the signal quality of the acquired biophysical signal can be affected by noise. Such noise, which can originate from various sources, can affect the evaluation of the patient, including the patient's biological lineage or clinical evaluation related to the lineage associated with such a signal, as well as any associated symptoms or medical conditions. In the case of cardiac signals, such noise can affect some or all of the acquired signal, reducing the effectiveness of the evaluation for CAD, arrhythmia, pulmonary hypertension, heart failure, and, for example, reducing the effectiveness of the evaluation for any symptom or medical condition related to, associated with, or (directly or indirectly) affecting the cardiac signal, thereby posing a risk of providing an incorrect evaluation and diagnosis to the patient.

[0006] In addition, when problems such as signal quality degradation have an adverse effect, some or all of the acquired signals may have to be ignored and new signals acquired from the patient. In some cases, this may require reacquiring the evaluation, causing inconvenience to the patient by having to return to the clinic, hospital, or other medical site, and potentially incurring additional costs to the medical system.

Summary of the Invention

Means for Solving the Problems

[0007] The exemplary methods and systems described herein facilitate quantifying the signal quality of acquired signals for evaluation and for gating the acquired signals for subsequent analysis.

[0008] As used herein, the term "cardiac signal" refers to one or more signals related to the structure, function, and / or activity of the cardiovascular system, including aspects related to the electrical / electrochemical conductivity of the signal, such as aspects that cause myocardial contraction. In some embodiments, the cardiac signal may include an electrocardiogram (ECG) signal, such as one acquired via an electrocardiogram or other modality.

[0009] As used herein, the term "neurological signal" refers to one or more signals related to the structure, function, and / or activity of the central and peripheral nervous systems, including the brain, spinal cord, nerves, and associated neurons and other structures, including aspects related to the electrical / electrochemical conduction of the signal. In some embodiments, the neurological signal may include an electroencephalogram (EEG) signal, such as one acquired via an electroencephalogram or other modality.

[0010] As used herein, the term "biophysical signal" is not limited to cardiac signals, neurological signals, or photoplethysmographic signals, but encompasses any physiological signal from which information can be obtained. Although not intended to be limiting by way of example, biophysical signals can be classified into types or categories, which can include, for example, electrical (e.g., signals related to specific cardiac and nervous systems that can be observed, identified, and / or quantified by techniques such as measurements of voltage / potential, 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, transmittance, visual observation, photoplethysmography, and the like), acoustic, chemical, mechanical (e.g., signals related to fluid flow, pressure, movement, vibration, displacement, strain), thermal, electrochemical (signals that can be correlated to the presence of specific analytes such as glucose). Biophysical signals can, in some cases, be described in the context of physiological systems (e.g., respiratory system, circulatory system (cardiovascular, pulmonary), nervous system, lymphatic system, endocrine system, digestive system, excretory system, muscular system, skeletal system, renal / urinary / excretory system, immune system, integumentary / exocrine system, genital system), organ systems (e.g., signals that can be specific to the heart and lungs when they work together), or in the context of tissues (e.g., muscle, fat, nerve, connective tissue, bone), cells, organelles, molecules (water, proteins, fats, carbohydrates, gases, free radicals, inorganic ions, minerals, acids, other compounds, elements, and their elementary particle components). Unless otherwise specified, the term "acquisition of biophysical signals" generally refers to any passive or active means for acquiring biophysical signals from physiological systems, such as mammals or non-mammalian organisms. Passive acquisition of biophysical signals generally refers to observing natural or induced electrical, magnetic, optical, and / or acoustic emissions with respect to body tissues.Non-limiting examples of passive and active biophysical signal acquisition means include, for example, voltage / potential, current, magnetic, acoustic, optical, and other passive methods for observing natural emissions with respect to body tissue, and in some cases, methods for inducing such emissions. Non-limiting examples of passive and active biophysical signal acquisition means include, for example, ultrasound, radio waves, microwaves, infrared and / or visible light (e.g., for use in pulse oximetry or photoplethysmography), visible light, ultraviolet light, and other methods for actively investigating body tissue without accompanying ionizing energy or radiation (e.g., X-rays). Active biophysical signal acquisition may also include transmitting ionizing energy or radiation (e.g., X-rays) (also referred to as "ionizing biophysical signals") to body tissue. Passive and active biophysical signal acquisition means can be performed invasively (e.g., via surgery or an invasive radiation intervention protocol) or non-invasively (e.g., via imaging).

[0011] Although the present disclosure is directed to the beneficial quantification of biophysical signal quality in the diagnosis and treatment of heart-related conditions and symptoms and / or nerve-related conditions and symptoms, such quantification can be applied to the diagnosis and treatment (including surgical, minimally invasive, and / or pharmacological treatments) of any condition and symptom in which a biophysical signal is involved in any relevant system of a living body. An example in the context of the heart is the diagnosis of CAD and any number of treatment methods, alone or in combination, such as the placement of a stent into a coronary artery, the performance of an atherectomy, the performance of an angioplasty, the prescription of drug therapy, and / or the prescription of exercise, nutrition, and other lifestyle changes. Other heart-related conditions or symptoms that can be diagnosed include, for example, arrhythmia, congestive heart failure, valvular insufficiency, 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 thrombus, and pulmonary hypertension due to other diseases or other disorders such as blood), and other heart-related conditions, symptoms, and / or diseases. Non-limiting examples of nerve-related diseases, conditions, or symptoms that can be diagnosed include, for example, epilepsy, schizophrenia, Parkinson's disease, Alzheimer's disease (and all other forms of dementia), autism spectrum (including Asperger's syndrome), attention deficit hyperactivity disorder, Huntington's disease, muscular dystrophy, depression, bipolar disorder, brain / spinal cord tumors (malignant and benign), movement disorders, cognitive impairments, speech impairments, various mental illnesses, brain / spinal cord / nerve injuries, chronic traumatic encephalopathy, cluster headaches, migraines, neuropathies (various forms including peripheral neuropathy), phantom limb / phantom limb pain, chronic fatigue syndrome, acute and chronic pain (including low back pain, failed back surgery syndrome, etc.), movement disorders, anxiety disorders, symptoms caused by infections or foreign substances (e.g., Lyme disease, encephalitis, rabies), narcolepsy and other sleep disorders, post-traumatic stress disorder, nerve symptoms / effects related to stroke, aneurysm, hemorrhagic injury, etc., tinnitus and other hearing-related diseases / symptoms, and vision-related diseases / symptoms.

[0012] Skeleton-muscle related signals (e.g., electromyogram (EMG), etc.) are often characterized as "in-band noise" with respect to cardiac signals, nerve signals, etc., that is, they often occur in the same or similar frequency ranges within the acquired biophysical signals of interest. For example, in the case of cardiac signals, the main frequency components of the generated signals are often about 0.5 Hz to about 80 Hz. Also, in the case of neurological signals such as brain signals, the frequency components are often about 0.1 Hz to about 50 Hz. Also, depending on the degree of contamination, the skeleton-muscle related signals can also have the same or similar amplitudes as typical heart-based waveforms and nerve-based waveforms, etc. In fact, if the skeleton-muscle related signals are similar to cardiac signals, neurological signals, and other biophysical signals, etc., it can cause significant problems with respect to the analysis of the biophysical signals of interest. Therefore, quantifying the signal quality of the measured biophysical signals can be important, for example, for evaluating the quality of the acquired biophysical signals of interest, for rejecting the use of the contaminated acquired signals in subsequent analysis, and for providing useful information in subsequent analysis to enable compensation for contamination, etc.

[0013] The methods and systems described in various embodiments herein are not so limited and may be utilized in any situation regarding one or more other physiological systems, organs, tissues, cells, etc. of a living body. By way of example only, two types of biophysical signals that may be useful in a cardiovascular situation are cardiac signals that can be acquired via conventional electrocardiogram (ECG / EKG) equipment, bipolar broadband biopotential (cardiac) signals that can be acquired from other devices such as those described herein, and signals that can be acquired by various plethysmograph techniques such as, for example, photoplethysmography.

[0014] In the context of the present disclosure, techniques for acquiring and analyzing biophysical signals are described particularly with regard to their use for diagnosing the presence, absence, localization (where applicable), and / or severity of specific disease states or disease symptoms in, associated with, or affecting the cardiovascular (or cardiac) system, including, for example, pulmonary hypertension (PH), coronary artery disease (CAD), and heart failure (e.g., left-sided or right-sided heart failure).

[0015] Pulmonary hypertension, heart failure, and coronary artery disease are three diseases / conditions related to the cardiovascular or cardiac system. Pulmonary hypertension (PH) generally refers to high blood pressure in the arteries of the lungs and can include various symptoms. PH typically has a complex and multifactorial etiology and presents with a latent clinical onset, although there are differences in severity. PH can cause complications such as right heart failure and often leads to death. The World Health Organization (WHO) classifies PH into five groups or types. The first PH group classified by WHO is pulmonary arterial hypertension (PAH). PAH is a chronic disease in which, among other things, the walls of the pulmonary arteries constrict and become stiff, and it is currently an incurable disease. Diagnosis of PAH requires at least a cardiac catheterization. PAH is characterized by vascular damage to the pulmonary arteries and is defined as having a mean pulmonary artery pressure of 25 mmHg or more in cardiac catheterization. One form of pulmonary arterial hypertension is known as idiopathic pulmonary arterial hypertension, which is PAH that develops without an identifiable cause. In particular, the subcategories of PAH include hereditary PAH, drug- and toxin-induced PAH, and PAH associated with other systemic diseases such as connective tissue diseases, HIV infection, portal hypertension, and congenital heart diseases. PAH includes all causes that lead to structural narrowing of the pulmonary vessels. In the case of PAH, the progressive narrowing of the pulmonary arterial bed is due to an imbalance of vasoactive mediators including prostacyclin, nitric oxide, and endothelin-1. As a result, the afterload on the right ventricle increases, causing right heart failure and leading to premature death. The second PH group classified by WHO is pulmonary hypertension due to left heart disease. Disorders in this group generally feature problems on the left side of the heart. Such problems can cause changes in the pulmonary arteries over time. Specific subgroups include left ventricular systolic dysfunction, left ventricular diastolic dysfunction, valvular heart disease, and also congenital cardiomyopathy and obstruction not due to valvular heart disease. Treatment of this second PH group tends to focus on the underlying problem (e.g., surgery to replace a heart valve, various drug therapies, etc.). The third PH group classified by WHO is large and diverse and is generally associated with lung diseases or hypoxia.The subgroups include chronic obstructive pulmonary disease, interstitial lung disease, sleep disordered breathing, alveolar hypoventilation disorder, chronic high altitude exposure, and developmental lung disease. The 4th PH group is what the WHO classifies as chronic thromboembolic pulmonary hypertension, which is caused by the obstruction of blood flow through the pulmonary artery by the entry or formation of blood clots in the lungs. The 5th PH group is classified by the WHO as including rare diseases that cause PH, such as blood diseases, systemic diseases such as sarcoidosis with lung lesions, metabolic diseases, and other disease subgroups. The mechanism of PH in this 5th group is not well understood.

[0016] Because the most common symptoms of PH (shortness of breath, fatigue, chest pain, edema, palpitations, dizziness) are associated with a very large number of other medical conditions, it can be difficult to diagnose all forms of PH in routine examinations. Blood tests, chest X-rays, electrocardiograms and echocardiograms, pulmonary function tests, exercise tolerance tests, and nuclear medicine tests are all used in various ways to help physicians diagnose PH in specific forms. As mentioned above, the "gold standard" for diagnosing PH, especially for diagnosing PAH, is right heart catheterization by directly measuring the pressure in the pulmonary artery. When a subject is suspected of having PAH, several tests such as electrocardiogram tests, chest X-ray tests, pulmonary function tests, etc. may be performed to confirm the condition. Evidence of right heart strain in electrocardiogram tests and evidence of pulmonary artery elevation or heart hypertrophy in chest X-ray tests are typically seen. However, even if the electrocardiogram and chest X-ray pictures are normal, the diagnosis of PAH cannot necessarily be excluded. Further tests may be required to confirm the diagnosis and determine the cause and severity. For example, blood tests, exercise stress tests, and nocturnal oxygen concentration tests may be performed. Additionally, imaging tests may be performed. Examples of imaging tests include isotope perfusion lung scans, high-resolution computed tomography, computed tomography pulmonary angiography, and magnetic resonance pulmonary angiography. When the diagnosis of PAH is made by these (and sometimes other) non-invasive tests, right heart catheterization is typically required to confirm the diagnosis by directly measuring the pulmonary artery pressure. Also, measurement of cardiac output and estimation of left atrial pressure using pulmonary artery wedge pressure are possible. Although there are non-invasive techniques for determining whether a subject may have PAH, these techniques cannot reliably confirm the diagnosis of PAH unless an invasive right heart catheterization is performed. Aspects and embodiments regarding methods and systems for evaluating PH are disclosed in commonly owned U.S. Patent Application No. 16 / 429,593, which is hereby incorporated by reference in its entirety.

[0017] Heart failure affects approximately 6 million people in the United States alone, and more than 870,000 people are diagnosed with heart failure each year. The term "heart failure" (sometimes referred to as congestive heart failure or CHF) generally refers to a chronic and progressive condition or process in which the heart muscle is unable to pump sufficient blood to meet the body's demands due to the weakening or stiffening of the heart muscle, or the presence of defects that impede proper circulation. This results in, for example, the retention of blood and body fluids in the lungs, edema, fatigue, dizziness, fainting, rapid and / or irregular heartbeat, dry cough, nausea, and shortness of breath. Common causes of heart failure are coronary artery disease (CAD), hypertension, cardiomyopathy, arrhythmia, kidney disease, heart defects, obesity, tobacco use, and diabetes. Diastolic heart failure (DHF), left or left-sided heart failure / disease (also referred to as left ventricular heart failure), right or right-sided heart failure / disease (also referred to as right ventricular heart failure), and systolic heart failure (SHF) are common types of heart failure.

[0018] Left-sided heart failure is further classified into two main types: systolic failure (or heart failure with reduced ejection fraction, or heart failure with reduced left ventricular function), and diastolic failure / dysfunction (or heart failure with preserved ejection fraction, or heart failure with preserved left ventricular function). Procedures and techniques commonly used to determine whether a patient has left-sided heart failure include cardiac catheterization, X-ray examination, echocardiogram, electrocardiogram (EKG), electrophysiology study, radionuclide imaging, and various treadmill tests, including tests that measure peak VO2. The ejection fraction (EF) is a measurement expressed as the percentage of the volume of blood pumped out (by the left ventricle in the case of left-sided heart failure) each time the ventricle contracts, and is most often obtained non-invasively by echocardiogram. The normal left ventricular ejection fraction (LVEF) ranges from approximately 55% to approximately 70%.

[0019] When systolic insufficiency occurs, the left ventricle is unable to contract with sufficient force to continue to circulate blood normally throughout the body, thereby preventing normal blood supply to the body. When it becomes difficult to compensate for the pumping function of the left ventricle, the left ventricle becomes weaker and thinner. As a result, blood flows backward into the organs, fluid accumulates in the lungs, or other parts of the body swell. Echocardiography, magnetic resonance imaging, and nuclear medicine scans (multi-gated acquisition, etc.) are techniques used to non-invasively measure the ejection fraction (EF), which is expressed as the percentage of blood pumped out of the left ventricle relative to the filling volume of the left ventricle and is useful for diagnosing systolic insufficiency. In particular, a left ventricular ejection fraction (LVEF) of less than 55% indicates that the pumping function of the heart is below normal, and in severe cases, it may be measured as less than about 35%. Generally, the diagnosis of systolic insufficiency can be made or supported when these LVEF values are below normal values.

[0020] When diastolic heart failure occurs, the left ventricle becomes stiff or thickened and loses its ability to relax normally, which means that the left lower chamber of the heart is not properly filled with blood. As a result, the amount of blood pumped out to the body decreases. If this continues, blood accumulates in the left atrium and further in the lungs, and body fluids accumulate, leading to symptoms of heart failure. In this case, the LVEF value tends to be maintained within the normal range. Therefore, it may be useful to diagnose diastolic heart failure and other forms of heart failure in which EF is maintained by measuring the left ventricular end-diastolic pressure (LVEDP) using other tests such as invasive catheterization. Usually, LVEDP is measured directly by placing a catheter into the left ventricle or indirectly by measuring the pulmonary capillary wedge pressure by placing a catheter into the pulmonary artery. Such catheter techniques, by their nature, tend to increase the risk of patient infections and other complications and also tend to be costly. Therefore, there is a need for non-invasive methods and systems for determining or estimating LVEDP when diagnosing the presence and / or severity of diastolic heart failure and also when diagnosing numerous other forms of heart failure in which EF is maintained. In addition, there is a need for non-invasive methods and systems for diagnosing the presence and / or severity of diastolic heart failure and for diagnosing numerous other forms of heart failure in which EF is maintained without necessarily including the determination or estimation of abnormal LVEDP. Embodiments of the present disclosure address all of these needs.

[0021] Right-sided heart failure often occurs due to left-sided heart failure when the weakened and / or stiffened left ventricle loses its ability to efficiently pump blood to the rest of the body. As a result, body fluids back up through the lungs, weakening the right side of the heart and causing right-sided heart failure. This backup causes fluid to flow backward in the veins, leading to fluid swelling in the legs, ankles, gastrointestinal tract, and liver. In other cases, certain lung diseases, such as chronic obstructive pulmonary disease and pulmonary fibrosis, can cause right-sided heart failure even when the left side of the heart is functioning normally. The procedures and techniques commonly used to determine whether a patient has left-sided heart failure include various forms of stress tests such as blood tests, cardiac CT scans, cardiac catheterizations, X-rays, coronary angiograms, echocardiograms, electrocardiograms (EKGs), myocardial biopsies, pulmonary function tests, and treadmill tests.

[0022] Pulmonary hypertension is closely related to heart failure. As described above, PAH (Group 1 of the WHO classification of PH) can lead to increased right ventricular afterload, right-sided heart failure, and early death. PH due to left-sided heart failure (Group 2 of the WHO classification of PH) is considered the most common cause of PH.

[0023] Ischemic heart disease, also known as cardiac ischemia or myocardial ischemia, and related symptoms or conditions can also be inferred or diagnosed using the techniques disclosed herein. Ischemic heart disease is a disease or group of diseases typically caused by coronary artery disease (CAD) and characterized by a reduced blood supply to the myocardium. CAD is closely related to and the most common cause of heart failure. CAD typically occurs when the inner lining of the coronary arteries, which supply blood to the myocardium, the muscle of the heart, develops atherosclerosis (the inner lining hardens or becomes stiff, plaques accumulate there, often with abnormal inflammation). Over time, CAD can weaken the myocardium and cause, for example, angina, myocardial infarction (heart attack), heart failure, and arrhythmias. An arrhythmia is an abnormal heart rhythm and can include some change from the normal electrical conduction sequence of the heart and, in some cases, can lead to cardiac arrest. The assessment of PH, heart failure, CAD, other diseases and / or symptoms can be complex, and as described above, many invasive techniques and tools are used to assess the presence and severity of symptoms. In addition, the commonality in symptoms regarding these diseases and / or conditions, and the fundamental cooperation between the respiratory and cardiovascular systems, have been pointed out as having complex physiological interrelationships because they cooperate with each other to supply oxygen to the body's cells and tissues, and may be utilized to improve the detection and ultimate treatment of such diseases and / or conditions. In this context, conventional methodologies for evaluating these biophysical signals still pose significant challenges in providing medical practitioners with tools to accurately detect / diagnose the presence or absence of such diseases and symptoms.

[0024] For example, in electrocardiogram testing, which is a branch of cardiology that analyzes the electrical activity of the heart to obtain information about the heart's structure and function, it has been observed that severe ischemic heart disease changes the ventricular conduction characteristics of the myocardium in the perfusion bed located downstream of the stenosis or occlusion of the coronary artery. The disease state can manifest at different locations in the heart and at different stages of severity, making accurate diagnosis difficult. Furthermore, the electrical conduction characteristics of the myocardium can vary from person to person, and other factors, such as measurement variations related to the placement of measurement probes and parasitic losses related to such probes and their associated components, can also affect the biophysical signals obtained during electrophysiological testing of the heart. Moreover, when the conduction characteristics of the myocardium are obtained as a relatively long cardiac phase gradient signal, they may exhibit complex non-linear variations that cannot be efficiently obtained by conventional modeling techniques.

[0025] In one aspect, a method for obtaining a biophysical signal dataset for clinical analysis (e.g., as part of a machine learning dataset or for clinical diagnosis) is disclosed. The method includes obtaining, by a processor, a biophysical signal dataset or a portion thereof regarding a subject (e.g., regarding the subject's heart, brain, lungs, etc.) for measurement, obtaining the biophysical signal dataset or portion via one or more surface probes of a non-invasive measurement system (e.g., disposed on the subject's chest) on one or more corresponding channels, and obtaining over an acquisition duration suitable for subsequent evaluation (e.g., greater than about 120 seconds, e.g., about 210 seconds), where the acquisition duration is predefined or dynamically determined or set by a user; determining, by a processor (e.g., a processor of the non-invasive measurement system), one or more signal quality parameters regarding the obtained biophysical signal dataset, where at least one of the one or more signal quality parameters is selected from the group consisting of a power line interference parameter related to power line noise contamination, a high-frequency noise parameter related to high-frequency noise contamination, a noise burst parameter related to high-frequency noise burst contamination, a sudden movement parameter related to sudden movement contamination, and an asynchronous noise parameter related to skeletal muscle contamination or cardiac cycle variation; rejecting, by the processor, the obtained biophysical signal dataset or an evaluated portion thereof when the one or more signal quality parameters fail a noise quality evaluation performed on the one or more signal quality parameters (e.g., the rejection causes the processor to output a visual indicator regarding the evaluation that failed in the non-invasive measurement system, an audio indicator regarding the evaluation that failed in the non-invasive measurement system, or a report regarding the evaluation that failed in the non-invasive measurement system, and this output is performed simultaneously with or substantially simultaneously with the measurement) (e.g., the rejection prompts the acquisition of a second biophysical signal dataset or a portion thereof regarding the subject immediately after the acquisition of the biophysical signal) (e.g.,An assessment of non-rejection, i.e., acceptance, of the acquired biophysical signal dataset includes causing the processor to transmit the acquired biophysical signal dataset over a network for remote clinical analysis.

[0026] In some embodiments, the method further includes outputting one or more of a visual indicator, an audio indicator, a vibration indicator, and a report regarding a failed assessment in the non-invasive measurement system, the output being simultaneous or near simultaneous with the measurement (e.g., to prompt acquisition of a second biophysical signal data set or portion thereof for the subject immediately following acquisition of the biophysical signal).

[0027] In some embodiments, the method further includes transmitting, by the processor, the acquired biophysical signal dataset over a network for remote clinical analysis following an assessment of non-rejection, i.e., following an assessment of acceptance, for the acquired biophysical signal dataset.

[0028] In some embodiments, the method further includes acquiring voltage gradient signals on one or more channels by one or more acquisition circuits of the measurement system, the voltage gradient signals being acquired at a frequency greater than about 1 kHz, and generating an acquired biophysical data set from the acquired voltage gradient signals by the one or more acquisition circuits. In some embodiments, the method further includes positioning at least a first surface probe on a first axis passing through the subject's body from left to right, positioning at least a second surface probe on a second axis passing through the subject's body from top to bottom, and positioning at least a third surface probe on a third axis passing through the subject's body from front to back, the first axis, the second axis, and the third axis being mutually orthogonal.

[0029] In some embodiments, when the power line interference parameters for any one or more of the channels fail the power line interference condition (e.g., when exceeding the power line interference threshold), the acquired biophysical signal data set or evaluation portion is rejected.

[0030] In some embodiments, when the high-frequency noise parameters related to high-frequency noise contamination for any one or more of the channels fail the high-frequency noise condition (e.g., when the high-frequency noise score exceeds a predetermined high-frequency noise threshold), the acquired biophysical signal data set or evaluation portion is rejected.

[0031] In some embodiments, when the noise burst parameters related to high-frequency noise burst contamination for any one or more of the channels fail the noise condition, the acquired biophysical signal data set or evaluation portion is rejected (e.g., using a high-frequency time series to test a 1-second window, comparing the 1-second window to the median of the high-frequency energy, and rejecting the biophysical signal data set when the 1-second energy is greater than twice the median).

[0032] In some embodiments, when the burst movement parameters related to burst movement contamination for any one or more of the channels fail the burst movement condition (e.g., when the baseline within a 1-second window of the signal changes by more than 25% of the ventricular depolarization amplitude of the channel with respect to the previous window), the acquired biophysical signal data set or evaluation portion is rejected.

[0033] In some embodiments, when the asynchronous noise parameters that may include skeletal muscle contamination or cardiac cycle variation for any one or more of the channels fail the asynchronous noise condition (e.g., when the periodic variation noise exceeds a predetermined threshold), the acquired biophysical signal data set or evaluation portion is rejected.

[0034] In some embodiments, the power line coefficient is determined by the processor performing a Fourier transform (e.g., a fast Fourier transform) on the acquired biophysical signal dataset or a portion thereof, and the processor determining the maximum value of the power line energy in a plurality of frequency ranges (e.g., about 50 Hz at about 48 Hz to about 52 Hz, about 60 Hz at about 58 Hz to about 62 Hz, about 150 Hz at about 145 Hz to about 155 Hz, about 180 Hz at about 175 Hz to about 185 Hz, and about 300 Hz at about 295 Hz to about 305 Hz).

[0035] In some embodiments, the assessment serves as a gating stage for subsequent analysis of a subject with respect to coronary artery disease, pulmonary hypertension, or other medical conditions or disease states.

[0036] In some embodiments, the received biophysical signal dataset includes a cardiac signal dataset.

[0037] In some embodiments, the biophysical signal dataset is generated substantially in real time with respect to acquiring the biophysical signal.

[0038] In some embodiments, the biophysical signal is acquired from a sensor within a smart device or within a handheld medical diagnostic device.

[0039] In some embodiments, the biophysical signal dataset includes broadband phase gradient cardiac signal data derived from biopotential signals simultaneously acquired from a plurality of surface electrodes disposed on the body surface proximate to the subject's heart.

[0040] In another aspect, a method for rejecting an acquired biophysical signal is disclosed. The method includes receiving, by a processor, a biophysical signal dataset of a subject; comparing, by the processor, the received biophysical signal dataset with at least one of power line interference, high-frequency noise, high-frequency noise bursts, abrupt baseline shifts, and periodic variations; and rejecting, by the processor, the received biophysical signal dataset based on the comparison.

[0041] In some embodiments, comparing the received biophysical signal dataset with power line interference includes determining a power line coefficient of the biophysical signal dataset, and rejecting the received biophysical signal dataset includes rejecting the biophysical signal dataset when the power line coefficient exceeds a predetermined threshold.

[0042] In some embodiments, comparing the received biophysical signal dataset with high-frequency noise includes determining a high-frequency noise score of the biophysical signal dataset, and rejecting the received biophysical signal dataset includes rejecting the biophysical signal dataset when the high-frequency noise score exceeds a predetermined threshold.

[0043] In some embodiments, comparing the received biophysical signal dataset with high-frequency noise bursts includes determining a high-frequency noise burst of the biophysical signal dataset using a high-frequency time series to test a one-second window, comparing the one-second window with a threshold, and rejecting the received biophysical signal dataset includes rejecting the biophysical signal dataset when the one-second energy is greater than the threshold.

[0044] In some embodiments, comparing the received biophysical signal dataset to a sudden baseline shift involves determining a sudden baseline shift when the baseline within a predetermined time window of the signal changes by more than a predetermined amount relative to the previous window, and rejecting the received biophysical signal dataset involves rejecting the biophysical signal dataset when a sudden baseline shift is determined.

[0045] In some embodiments, comparing the received biophysical signal dataset to a periodic variation involves determining periodic variation noise, and rejecting the received biophysical signal dataset involves rejecting the biophysical signal dataset when the periodic variation noise exceeds a predetermined threshold.

[0046] In some embodiments, the comparison involves determining the presence of asynchronous noise within the acquired biophysical signal dataset that has a value or energy exceeding a pre-specified threshold.

[0047] In some embodiments, the method further includes generating, by a processor, a notification that the acquisition of the biophysical signal dataset was unsuccessful. In some embodiments, the notification prompts the subsequent execution of the acquisition of the biophysical signal dataset.

[0048] In some embodiments, the method further includes causing, by a processor, the received biophysical signal dataset to be transmitted via a network to an external analysis system, where the analysis system is configured to analyze the received biophysical signal data regarding the presence or extent of a medical condition or clinical symptom.

[0049] In another aspect, a system is disclosed that includes one or more processors and a memory having instructions stored on the one or more processors, wherein execution of the instructions by the one or more processors causes the one or more processors to perform any of the methods described above.

[0050] In another aspect, a non-transitory computer-readable medium having stored instructions is disclosed, wherein execution of the instructions by one or more processors causes the one or more processors to perform any of the methods described above. This specification also provides, for example, the following items. (Item 1) A method for obtaining a biophysical signal dataset for clinical analysis, comprising: obtaining, by a processor, a biophysical signal dataset or a portion thereof regarding a subject for measurement, wherein the biophysical signal dataset or the portion is obtained via one or more surface probes of a non-invasive measurement system on one or more corresponding channels and is obtained over an acquisition duration suitable for subsequent evaluation, and the acquisition duration is predefined or dynamically determined or set by a user; determining, by the processor and / or remotely by one or more cloud-based services or systems, one or more signal quality parameters regarding the obtained biophysical signal dataset, wherein at least one of the one or more signal quality parameters is selected from the group consisting of a power line interference parameter related to power line noise contamination, a high-frequency noise parameter related to high-frequency noise contamination, a noise burst parameter related to high-frequency noise burst contamination, a sudden movement parameter related to sudden movement contamination, and an asynchronous noise parameter related to skeletal muscle contamination or cardiac cycle variation; When the one or more signal quality parameters fail a noise quality assessment performed on the one or more signal quality parameters, the method includes rejecting, by the processor, the acquired biophysical signal data set or an evaluated portion thereof. (Item 2) The method according to item 1, further comprising outputting one or more of a visual indicator, an audio indicator, a vibration indicator, and a report regarding the assessment that failed in the non-invasive measurement system, and performing the output simultaneously with or substantially simultaneously with the measurement. (Item 3) The method according to item 1 or 2, further comprising transmitting, by the processor, the acquired biophysical signal data set via a network for remote clinical analysis after an assessment of non-rejection, i.e., acceptance, of the acquired biophysical signal data set. (Item 4) Acquiring a voltage gradient signal on the one or more channels by one or more acquisition circuits of the measurement system, and acquiring the voltage gradient signal at a frequency greater than about 1 kHz. The method according to any one of items 1 to 3, further comprising generating, by the one or more acquisition circuits, the acquired biophysical data set from the acquired voltage gradient signal. (Item 5) Arranging at least a first surface probe at a first axis along which the body of the subject passes from left to right. Arranging at least a second surface probe at a second axis along which the body of the subject passes from top to bottom. The method according to any one of items 1 to 4, further comprising arranging at least a third surface probe at a third axis along which the body of the subject passes from front to back, and setting the first axis, the second axis, and the third axis as axes orthogonal to each other. (Item 6) When the power line interference parameter for any one of the one or more channels fails to meet the power line interference condition, rejecting the acquired biophysical signal dataset or the evaluation part, according to any one of items 1 to 5. (Item 7) When the high-frequency noise parameter related to high-frequency noise mixing for any one of the one or more channels fails to meet the high-frequency noise condition, rejecting the acquired biophysical signal dataset or the evaluation part, according to any one of items 1 to 6. (Item 8) When the noise burst parameter related to high-frequency noise burst mixing for any one of the one or more channels fails to meet the noise condition, rejecting the acquired biophysical signal dataset or the evaluation part, according to any one of items 1 to 7. (Item 9) When the sudden movement parameter related to sudden movement mixing for any one of the one or more channels fails to meet the sudden movement condition, rejecting the acquired biophysical signal dataset or the evaluation part, according to any one of items 1 to 8. (Item 10) When the asynchronous noise parameter that may include skeletal muscle mixing or cardiac cycle variation for any one of the one or more channels fails to meet the asynchronous noise condition, rejecting the acquired biophysical signal dataset or the evaluation part, according to any one of items 1 to 9. (Item 11) The power line coefficient related to the power line interference parameter Executing a Fourier transform on the acquired biophysical signal dataset or the part thereof by the processor, Determining, by the processor, the maximum value of the power line energy in a plurality of frequency ranges, according to any one of items 1 to 10. (Item 12) The method according to any one of items 1 to 11, wherein the evaluation is a gating stage for subsequent analysis of the subject with respect to coronary artery disease or pulmonary hypertension. (Item 13) The method according to any one of items 1 to 12, wherein the received biophysical signal dataset includes a cardiac signal dataset. (Item 14) The method according to any one of items 1 to 13, wherein the biophysical signal dataset is generated substantially in real time with respect to obtaining a biophysical signal. (Item 15) The method according to any one of items 1 to 14, wherein the biophysical signal is obtained from a sensor within a smart device or within a handheld medical diagnostic device. (Item 16) The method according to any one of items 1 to 5, wherein the biophysical signal dataset includes broadband phase gradient cardiac signal data obtained simultaneously from a plurality of surface electrodes disposed on the body surface proximate to the heart of the subject. (Item 17) A method for rejecting an acquired biophysical signal, receiving, by a processor, a biophysical signal dataset of a subject, and comparing, by the processor and / or remotely by one or more cloud-based services or systems, the received biophysical signal dataset with at least one of power line interference, high-frequency noise, high-frequency noise bursts, sudden baseline shifts, and periodic variations, and rejecting, by the processor, the received biophysical signal dataset based on the comparison. (Item 18) Comparing the received biophysical signal data set with the power line interference is assumed to include determining a power line coefficient for the biophysical signal data set, and rejecting the received biophysical signal data set is assumed to include rejecting the biophysical signal data set when the power line coefficient exceeds a predetermined threshold, the method according to item 17. (Item 19) Comparing the received biophysical signal data set with the high-frequency noise is assumed to include determining a high-frequency noise score for the biophysical signal data set, and rejecting the received biophysical signal data set is assumed to include rejecting the biophysical signal data set when the high-frequency noise score exceeds a predetermined threshold, the method according to item 16 or 17. (Item 20) Comparing the received biophysical signal data set with the high-frequency noise burst is assumed to include determining a high-frequency noise burst for the biophysical signal data set using a high-frequency time series to test a 1-second window, and comparing the 1-second window with a threshold, and rejecting the received biophysical signal data set is assumed to include rejecting the biophysical signal data set when the 1-second energy is greater than the threshold, the method according to any one of items 16 to 19. (Item 21) Comparing the received biophysical signal data set with the abrupt baseline shift is assumed to include determining an abrupt baseline shift when the baseline within a predetermined time window of the signal changes by more than a predetermined amount relative to the previous window, and rejecting the received biophysical signal data set is assumed to include rejecting the biophysical signal data set when the abrupt baseline shift is determined, the method according to any one of items 16 to 20. (Item 22) Comparing the received biophysical signal dataset with the periodic variation includes determining the periodic variation noise, and rejecting the received biophysical signal dataset includes rejecting the biophysical signal dataset when the periodic variation noise exceeds a predetermined threshold. The method according to any one of items 16 to 21. (Item 23) The comparison includes determining the presence of asynchronous noise in the acquired biophysical signal dataset having a value or energy exceeding a pre-specified threshold. The method according to any one of items 16 to 22. (Item 24) Further including generating, by the processor, a notification indicating that the acquisition of the biophysical signal dataset was unsuccessful. The method according to any one of items 1 to 23. (Item 25) Prompting, by the notification, subsequent execution of the acquisition of the biophysical signal dataset. The method according to any one of items 1 to 24. (Item 26) Further including causing the processor to transmit, via a network, the received biophysical signal dataset to an external analysis system, and configuring the analysis system to analyze the received biophysical signal data regarding the presence or degree of a medical condition or clinical symptom. The method according to any one of items 1 to 25. (Item 27) A system comprising: One or more processors; A memory having instructions stored on the one or more processors, execution of the instructions by the one or more processors causing the one or more processors to execute the method according to any one of items 1 to 26. A system including a memory. (Item 28) A non-transitory computer-readable medium having stored instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of items 1 to 26.

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the specification, serve to explain the principles of the methods and systems included herein. The embodiments can be better understood based on the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0053] Each feature point described in this specification and all feature points, as well as each combination and all combinations of two or more of those feature points, are included within the scope of the present invention as long as the feature points included in such combinations do not conflict with each other.

[0054] As further described in this specification, the signal quality of the acquired signal is evaluated in real time, and if the acquired signal is corrupted by noise, a notification is generated and provided to the responsible technician. In one or more implementations, the evaluation includes (1) detection of power line interference, (2) detection of sudden movement, (3) detection of noise bursts, (4) confirmation of the minimum signal-to-noise ratio (SNR), and / or (5) detection of asynchronous noise (e.g., electromyogram (EMG) noise).

[0055] As further described herein, a signal is acquired and a determination is made in real time as to whether there are problems with the acquisition (e.g., the acquired signal is processed immediately to determine whether it is acceptable or unacceptable, or to determine whether the acquired signal is of sufficient quality for subsequent evaluation, etc.), and if there are problems, an output is provided to indicate that the signal acquisition needs to be performed again (i.e., if the acquired signal is unacceptable, the acquired signal is rejected).

[0056] FIG. 1A shows an exemplary system 100 configured to quantify and remove asynchronous noise, such as skeletal muscle-related artifact noise contamination, according to an exemplary embodiment, and to use such quantification to more accurately evaluate complex non-linear variations in a quasi-periodic system. As used herein, the term "remove" and other similar terms refer to significantly reducing noise contamination, either wholly or in part, to improve subsequent analysis or to be beneficial for subsequent analysis.

[0057] In FIG. 1A, a measurement system 102 acquires a plurality of biophysical signals 104 from a subject 106 via any number of measurement probes 114 (illustrated as including six such probes 114a, 114b, 114c, 114d, 114e, and 114f in the system 100 of FIG. 1), and generates a biophysical signal dataset 108 available in a non-invasive biophysical signal evaluation system 110 for determining a clinical output 112, which is a non-invasive embodiment (illustrated as a "measurement system (biophysical)" 102). In some embodiments, the clinical output includes an assessment of the presence or absence of a disease and / or physiological characteristics estimated with respect to the physiological system under study. In other embodiments, there is no clinical output, but rather information is output as information that a clinician can use to independently clinically evaluate information related to the patient for whom the signal is being evaluated.

[0058] In some embodiments, as shown in FIG. 1A, the measurement system 102 is configured to remove asynchronous noise contamination (e.g., via operation 118) from an amplified and digitized biophysical signal data set 117 that has been processed / conditioned by amplification and digitization operations 116 at the front end. The contamination removal operation 118 is based on quantifying asynchronous noise that may potentially be present within the data set 117. In some embodiments for removing asynchronous noise, operation 118 may be performed in near real time, for example, after a representative periodic data set has been established from some samples of the acquired biophysical signal data set 108, such as via a processor and corresponding instructions or via a digital circuit (e.g., a CPLD, microcontroller, and the like). The acquired biophysical signal data set 108 refers to any data set (e.g., 117, 108) generated by or within the measurement system 102 after the front-end amplification and digitization operations 116. In some embodiments, a representative periodic data set can be established by using hundreds of samples. In other embodiments, a representative periodic data set can be established by using thousands of samples. In some embodiments, the quantification of asynchronous noise is performed within a hardware circuit that is integrated into and operates with the front-end amplification and digitization operations 116.

[0059] In some embodiments, the measurement system 102 is configured to acquire a biophysical signal that can be based on the body's bioelectrical potential as a bioelectrical potential biophysical signal via a bioelectrical potential sensing circuit. In the context of the heart and / or electrocardiogram, the measurement system 102 is configured to acquire a heart-related bioelectrical potential signal or electrophysiological signal for a living subject (e.g., a human, etc.) as a bioelectrical potential heart signal dataset. In some embodiments, the measurement system 102 is configured to acquire a broadband heart phase gradient signal as a bioelectrical potential signal or other signal type (e.g., current signal, impedance signal, magnetic signal, optical signal, ultrasonic or acoustic signal, etc.). The term "broadband" with respect to the acquired signal and the corresponding dataset refers to a signal having a frequency range that is substantially larger than the Nyquist sampling rate of the maximum dominant frequency of the physiological system of interest. Typically, for a heart signal having dominant frequency components in the range of about 0.5 Hz to about 80 Hz, the broadband heart phase gradient signal or broadband heart biophysical signal is at about 0.1 Hz to about 1 kHz, about 0.1 Hz to about 2 kHz, about 0.1 Hz to about 3 kHz, about 0.1 Hz to about 4 kHz, about 0.1 Hz to about 5 kHz, about 0.1 Hz to about 6 kHz, about 0.1 Hz to about 7 kHz, about 0.1 Hz to about 8 kHz, about 0.1 Hz to about 9 kHz, about 0.1 Hz to about 10 kHz, and greater than about 0.1 Hz to 10 kHz (e.g., 0.1 Hz to 50 kHz, or 0.1 Hz to 500 kHz), and includes heart frequency information at frequencies selected from the group consisting of. In addition to acquiring the dominant frequency component, broadband acquisition also facilitates acquiring other frequencies of interest. Examples of such frequencies of interest can include, among other things, a QRS frequency profile (which can have a frequency range up to 250 Hz). The term "phase gradient" with respect to the acquired signal and the corresponding dataset refers to signals acquired from different perspectives of the body to observe phase information regarding a series of different events / functions of the physiological system of interest.After signal acquisition, the term "phase gradient" refers to preserving phase information using hardware, software, and techniques (e.g., phase linear filters, signal processing operators, and / or signal processing algorithms) for signal processing and preprocessing without distortion.

[0060] In a neurological context, the measurement system 102 is configured to acquire electrophysiological signals or bioelectrical signals related to neurology of a living subject (e.g., a human, etc.) as a neurological biophysical signal dataset. In some embodiments, the measurement system 102 is configured to acquire a broadband neurological phase gradient signal as a bioelectrical signal or other signal type (e.g., a current signal, an impedance signal, a magnetic signal, an ultrasonic, an optical signal, an ultrasonic or an acoustic signal, etc.). Examples of the measurement system 102 are described in U.S. Patent Application Publication No. 2017 / 0119272 and U.S. Patent Application Publication No. 2018 / 0249960, each of which is hereby incorporated by reference in its entirety.

[0061] In some embodiments, the measurement system 102 is configured to acquire a broadband biopotential biophysical phase gradient signal as an unfiltered electrophysiological signal such that one or more spectral components of the signal are not altered. In fact, in such embodiments, the broadband biopotential biophysical phase gradient signal is acquired, converted, and even analyzed (e.g., prior to digitization) without being filtered (e.g., via hardware circuitry and / or digital signal processing techniques, etc.) and without affecting the phase linearity of the biophysical signal of interest. In some embodiments, the broadband biopotential biophysical phase gradient signal is acquired at a microvolt or sub-microvolt resolution that is at or significantly below the noise floor of acquisition devices for conventional electrocardiograms, electroencephalograms, and other biophysical signals. In some embodiments, the broadband biopotential biophysical signal is sampled simultaneously while having a temporal skew or "lag" of less than about 1 microsecond, and in other embodiments, has a temporal skew or lag of not more than about 10 femtoseconds. Notably, the illustrated system is to minimize non-linear distortion (e.g., distortion that may be introduced via a particular filter) within the acquired broadband phase gradient signal so as not to affect the information contained therein.

[0062] Referring still to FIG. 1A, the evaluation system 110 is configured to receive the acquired biophysical signal dataset 108 (in this embodiment, the noise-removed dataset 108), for example, via a network or the like, and in some embodiments, is configured to generate one or more three-dimensional vector electrocardiogram datasets 122 by a conversion operation 120 (denoted as “phase space conversion” 120), and further, for example, is analyzed via one or more machine learning analysis operations and / or one or more prediction operations (illustrated as step 124) regarding the phase gradient biophysical signal dataset 108. Examples of the conversion operation and the machine learning / predictor operation will be described below and are also described in U.S. Patent Application Publication No. 2013 / 0096394, which is hereby incorporated by reference in its entirety. In some embodiments, the acquired biophysical signal dataset 108 is structured as a multi-dimensional dataset for subsequent processing without being explicitly converted, for example, when the intermediate dataset is not visualized.

[0063] In some embodiments, the measurement system 102 is configured to evaluate the signal quality of the acquired biophysical signals and, based on such an evaluation, reject some or all of the acquired signal dataset. FIG. 1B shows an exemplary system configured to reject the acquired biophysical signals based on evaluating the acquired biophysical signal quality by quantifying asynchronous noise and artifact contamination according to another exemplary embodiment. In some embodiments, the measurement system 102 is configured to perform an asynchronous noise removal operation 118 and a signal quality evaluation operation 130 based on the quantification of asynchronous noise.

[0064] In some embodiments, since the clinical analysis of the acquired biophysical signal 108 can be performed on a system separate from the measurement system 102 (e.g., the evaluation system 110), the signal quality check ensures that the acquired biophysical signal dataset 108 is suitable for subsequent clinical analysis. This operation can easily prompt the non-invasive measurement system 102 to reacquire the biophysical signal dataset, so that before the biophysical signal dataset undergoes further processing and analysis for clinical evaluation or becomes available for further processing and analysis for clinical evaluation, it is ensured that the acquired biophysical signal dataset is free from asynchronous noise (e.g., skeletal muscle-related noise, etc.).

[0065] In some embodiments, the signal quality assessment operation 130 is performed in substantially real time, e.g., in less than about 1 minute or in less than about 5 minutes. In response, the system 102 can prompt the reacquisition of the biophysical signal dataset. This substantially real-time assessment enables the reacquisition of the biophysical signal dataset, if necessary, before the patient leaves the examination room or before the patient leaves another location where the biophysical signal is acquired. The analysis performed by the evaluation system 110 to determine the clinical output is, in some embodiments, performed in approximately 10 to 15 minutes. In other embodiments, this analysis is performed in less than about 5 minutes. In still other embodiments, this analysis is performed in approximately 5 to 10 minutes. In yet other embodiments, this analysis is performed in more than about 15 minutes.

[0066] In some embodiments, signal evaluation is performed entirely at the same physical location as the patient (e.g., on one or more computing and / or storage devices located in the patient's bedroom or in the clinician's examination room). In some embodiments, signal evaluation is performed entirely at a physical location different from the patient's location (e.g., on one or more computing devices and / or storage devices located in a different room, a different building, a different state, a different country, etc.). In some embodiments, signal evaluation is performed in a networked environment that includes multiple physical locations as well as multiple computing devices and / or multiple storage devices. Such a networked environment can be configured to securely protect the privacy of the patient whose signals are being evaluated, for example, to comply with various privacy requirements.

[0067] In some embodiments, signal evaluation is performed, for example, at the same speed or approximately the same speed as the signal evaluation system is operable (e.g., in real time or near real time, depending on the configuration of the signal evaluation system, network constraints, etc.) when the signal is being acquired from the patient. In other embodiments, signal evaluation is performed partially when the signal is being acquired and partially after the signal has been acquired from the patient and stored. In yet other embodiments, no signals are evaluated when they are being acquired from the patient; instead, they are stored for later evaluation compared to the time when they were acquired from the patient. Of course, all signals may be stored after acquisition for later evaluation or re-evaluation, regardless of the time at which they can be evaluated.

[0068] One or more clinicians may perform a clinical evaluation of a patient, based in whole or in part on the signal evaluation of the patient performed by the systems and methods described herein. Such clinicians may be physically with the patient and / or may be located at a location physically remote from the patient. The signal evaluation system described herein may also perform, in whole or in part, a clinical evaluation of the patient, for example, by the clinical output of one or more operations performed by the signal evaluation system. Alternatively, the signal evaluation system may simply provide information that is less than a clinical evaluation for use by a clinician when performing an independent clinical evaluation of the patient. And when the signal evaluation system provides a clinical output, the clinician may choose to receive or reject such clinical output when performing an independent final clinical evaluation of the patient, for example, in cases where such clinician involvement and final decision-making is desired or (e.g., by law, protocol, insurance requirements, etc.) required.

[0069] In some embodiments, the non-invasive measurement system 102 is configured to generate a notification 126 (captioned in FIG. 1B as "indicate that the signal quality assessment has failed") that the acquisition of the biophysical signal dataset was unsuccessful or that such acquisition was inappropriate, and this notification may also prompt a reacquisition of the biophysical signal. The notification may be in any form, for example, a visual output (e.g., one or more indicator lights or indicators on a screen), an audio output, a tactile / vibratory output (or any combination thereof), etc., provided to a technician or clinician and / or provided to a patient. Examples of user interfaces (e.g., graphical user interfaces) of the measurement system 102 that may present the notification 126 are described in U.S. Patent Application Publication No. 2017 / 0119272, titled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition," filed on Aug. 26, 2016, and U.S. Design Application No. 29 / 578,421, titled "Display with Graphical User Interface," each of which is incorporated herein by reference in its entirety. For this purpose, all or part of the rejected biophysical signal dataset may not be used in subsequent analysis (e.g., 120, 124) to obtain the clinical output 112.

[0070] In some embodiments, the rejected biophysical signal dataset may optionally be stored in any suitable memory 128 for further (troubleshooting) analysis (132) regarding the defects and / or other reasons that caused the acquired signal to be rejected. For this purpose, all or part of the rejected biophysical signal dataset may not be used in subsequent analysis (e.g., 120, 124) to obtain the clinical output 112, depending on the outcome of any such analysis 132.

[0071] In some embodiments, system 200 may use all or part of the rejected biophysical signal data set in subsequent analysis (e.g., 120, 124) to obtain clinical output 112 or, for example, to improve the operating capabilities of system 200 and the like.

[0072] In other embodiments, a clinician or other operator may control, either alone or in association with or assisted by system 200, whether all or part of the rejected biophysical signal data set can be used or not, and how it is used or not used.

[0073] FIG. 2 is a diagram showing an exemplary evaluation system 200 according to an exemplary embodiment. In system 200, different components of a coronary artery disease (CAD) evaluation algorithm are assembled to provide an evaluation of CAD. This starts with the reception of signals and ends by returning a set consisting of the final evaluation of CAD, the localization of that CAD with respect to one or more diseased arteries, and / or one or more phase space tomography data sets / images (also referred to as "PST data sets / images"). Thus, system 200 may be used, for example, to determine whether there is a lesion in any of the coronary arteries of a subject. The CAD localization evaluation may also be provided, for example, when coronary artery disease is determined, and its presence may be localized to appropriate coronary arteries such as the left circumflex artery (LCX), left anterior descending artery (LAD), right coronary artery (RCA), other arteries, or some combination of these. In addition, the PST data set / image may, for example, have a two-dimensional or three-dimensional graphic representation regarding the evaluation generated and output via phase space analysis.

[0074] Useful concepts of phase space and examples of analysis are described in U.S. Patent Application Publication No. 2018 / 0000371, entitled "Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation", U.S. Patent Application Publication No. 2019 / 0214137, entitled "Method and System to Assess Disease Using Phase Space Volumetric Objects", filed on December 26, 2018, and U.S. Patent Application Publication No. 2019 / 0200893, entitled "Method and System to Assess Disease Using Phase Space Tomography and Machine Learning", and each of these patent documents is hereby incorporated by reference in its entirety.

[0075] In some embodiments, the system 200 includes a healthcare provider portal (also referred to herein as the "portal") configured to display, in a phase space analysis and / or an angiography equivalent report, a stored phase space data set / image and / or a clinical output 112, such as an assessment of the presence and / or absence of a disease and / or physiological characteristics (among other intermediate data sets) estimated with respect to a physiological system under study. The healthcare provider portal, in some embodiments, may be referred to as a physician portal or a clinician portal and is configured to access, obtain, and / or display or present reports and / or phase space volume data sets / images and / or clinical outputs 112 (and other data) for the reports from a repository (e.g., a storage area network).

[0076] In some embodiments, the healthcare provider portal is configured to display the phase space volume dataset / image (or an intermediate dataset derived therefrom) and / or the clinical output 112, within and / or together with, an anatomical mapping report, a coronary artery tree report, and / or a 17-segment report. The healthcare provider portal may present the data, for example, in real-time (e.g., as a web object), as an electronic document, and / or in other standardized or non-standardized dataset visualization / image visualization / medical data visualization / scientific data visualization formats. The healthcare provider portal, in some embodiments, is configured to access and obtain reports and / or phase space volume datasets / images and / or clinical outputs (and other data) for the reports from a repository (e.g., a storage area network). The healthcare provider portal and / or the repository can comply with laws and regulations regarding the privacy of patient information and other personal data (e.g., the Health Insurance Portability and Accountability Act of 1996 in the United States, and the EU General Data Protection Regulation, etc.), as well as laws regarding the sale of medical devices (e.g., the United States Federal Food, Drug, and Cosmetic Act, and the EU Medical Device Regulation, etc.). Further description of an exemplary healthcare provider portal is described in U.S. Patent Application Publication No. 2018 / 0078146, entitled "Method and System for Visualization of Heart Tissue at Risk", which patent document is hereby incorporated by reference in its entirety. In certain embodiments, the healthcare provider portal is configured to present patient medical information to healthcare providers, while in other embodiments, the healthcare provider portal can be made accessible to patients, researchers, academics, and / or other portal users.

[0077] An anatomical mapping report, in some embodiments, includes one or more depictions of a rotatable and optionally scalable three-dimensional anatomical map of the cardiac regions of the affected myocardium. The anatomical mapping report, 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 the myocardium. A coronary artery tree report, in some embodiments, includes one or more two-dimensional views of the major coronary arteries. A 17-segment report, in some embodiments, includes one or more two-dimensional 17-segment views of the corresponding regions of the myocardium. In each of the reports, values indicating the presence of a heart disease or condition at a location within the myocardium, and labels indicating the presence of a heart disease, may be rendered as both static and dynamic visualization elements indicating the predicted occluded regions, using, for example, color highlights for regions of the affected myocardium, and animation sequences that highlight regions of one or more of the affected coronary arteries. In some embodiments, each of the reports includes a text label for indicating the presence or absence of a heart disease (e.g., the presence of severe coronary artery disease, etc.), and a text label for indicating the presence (i.e., location) of a heart disease in a given coronary artery disease.

[0078] In the context of the cardiovascular system, in some embodiments, the healthcare provider portal (and corresponding user interface) is configured to present a summary information visualization of myocardial tissue identifying at-risk myocardium and / or occluded coronary arteries. The user interface can be a graphical user interface (“GUI”) with a touch-sensitive or pre-touch-sensitive screen having an input function. The user interface can be used, for example, to direct patient diagnosis and treatment and / or to evaluate patients under study. The visualization may include multiple depictions for a given report regarding a rotatable three-dimensional anatomical map of the cardiac regions of the 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, thereby facilitating the interpretation and evaluation of structural landmarks of the myocardium to characterize abnormalities in cardiac and cardiovascular function.

[0079] In the embodiment shown in FIG. 2, the execution of the evaluation system 200 is gated at its start by a requirement that the signal be of sufficient quality for subsequent evaluation, as further explained in some embodiments. In other embodiments of the system 200, this signal quality requirement may be optional (e.g., selectively activated by the system 200 and / or the user), or may not exist at all.

[0080] In system 200, the phase signal recorder (PSR) signal 205 may be received as an input, together with data indicating the gender and / or age 210 of a subject (e.g., a patient). In one embodiment, the PSR signal 205 (and its corresponding data set) is an unmodified signal that is an example of a broadband phase gradient biophysical signal. In a particular implementation, data from three channels named "ORTH1", "ORTH2", and "ORTH3" (related to signal 205) are analyzed from a PSR file downloaded from a storage area network (e.g., referred to as a "phase signal data repository" (PSDR)). As schematically shown in FIGS. 1A and 1B, a set of six probes or electrodes (e.g., probes 114a - 114f) are placed on the subject 106. These electrodes 114 may be arranged, for example, along three orthogonal axes with respect to the subject's body. In one implementation, the data from channel "ORTH1" corresponds to a bipolar acquisition channel data series recorded by a phase space recorder from electrodes 114 arranged along or in the vicinity of one of these orthogonal axes passing through the subject's body from the left side to the right side of the subject. Also, the data from channel "ORTH2" corresponds to a second bipolar acquisition channel data series recorded by a phase space recorder from two other electrodes 114 arranged along or in the vicinity of a second of these orthogonal axes passing through the subject's body from the upper side to the lower side of the subject. And the data from channel "ORTH3" corresponds to a third bipolar acquisition channel data series recorded by a phase space recorder from two further electrodes 114 arranged along or in the vicinity of a third of these orthogonal axes passing through the subject's body from the front to the back of the subject. The signals of ORTH1, ORTH2, and ORTH3, and their corresponding data sets can be arranged, for example, within mutually orthogonal axes in phase space coordinates.Note that in some embodiments, the ORTH1 signal, the ORTH2 signal, and the ORTH3 signal (e.g., examples of broadband phase gradient biophysical signals) are referred to in order to be more clearly distinguished from the vector electrocardiogram device.

[0081] As will be further described in some embodiments with respect to FIGS. 3-21, for example, the signal quality evaluation system module 300 that may be provided within the measurement system 102 of FIG. 1 to perform operation 130 determines whether subsequent processing may proceed by evaluating the input signal 383 (e.g., the PSR signal 205, etc.), or whether the signal is unacceptable (e.g., too noisy) and another input signal should be acquired before proceeding with subsequent processing. Thus, in the illustrated embodiment, the output of the signal quality evaluation system module 300 is an evaluation of whether to proceed with further analysis (e.g., performed by the intermediate processing components 220-240 as understood from FIG. 2) and the determination of a time window for the signal suitable for analysis. If the evaluation indicates, for example, that the acquired PSR signal 205 is very suitable (i.e., acceptable), then the PSR signal, its corresponding dataset, and the determined time window are provided for subsequent processing by the intermediate processing components; otherwise, the signal quality evaluation system module 300 indicates (e.g., to the user) that the acquired PSR signal (e.g., signal 205) is not suitable (i.e., unacceptable) and that another PSR signal must be acquired to proceed with subsequent processing. As described above, in other embodiments, the unacceptable signal quality indication rendered by the module 300 may be overwritten by the system 200 and / or the user, and some or all of the input signal 383 may be used in subsequent processing. Additionally, regardless of whether all or part of the input signal 383 is used in relation to the signal quality evaluation system module 300, such signal 383 may optionally be stored in memory, either in whole or in part, for analysis and / or for future use.

[0082] In the embodiment of FIG. 2, the intermediate processing components include a CNN (Convolutional Neural Network) prediction module 220, a visual prediction generation module 225, a metadata prediction generation module 230, a prediction combination module 235, and a CVMGD (Cycle-Variation-Mediated Gender-Dependence) compensation module 240. Some of all these intermediate processing components or modules may or may not exist in other configurations of the system 200 as assumed in other embodiments.

[0083] The CNN prediction generation module 220 receives, as inputs, from the signal quality evaluation module 300, the uncorrected PSR signal 205 (channel data from ORTH1 and ORTH3 analyzed from the PSR file downloaded from PSDR) and a time window regarding the signal suitable for analysis. The output of the CNN prediction generation module 220 is, in one exemplary implementation, the overall CAD evaluation using, for example, the weighted sum of two individual CNN models such as CNN model 129 and CNN model 85 Other embodiments of the system 200 assume the use of one CNN or the use of three or more CNNs.

[0084] The visual prediction generation module 225 receives, as an input, from the signal quality evaluation 300, the uncorrected PSR signal 205 (channel data from ORTH1, ORTH2, and ORTH3 analyzed from the PSR file downloaded from PSDR), and outputs an overall CAD evaluation (hereinafter referred to as "visual feature point evaluation") by extracting visual feature points and further applying a linear relationship over those visual feature points.

[0085] The metadata prediction generation module 230 receives the uncorrected PSR signal 205 as an input from the signal quality evaluation module 300. The module 230 also receives, as inputs, data 210 related to or indicative of the gender (i.e., biological sex) and / or age of the subject. The module 230 outputs an overall CAD evaluation for the subject through the use of a linear relationship (referred to as "metadata" in the following exemplary equation) that takes into account data regarding gender and / or age.

[0086] In the embodiment of FIG. 2, the prediction combination module 235 receives, as inputs, the overall CAD evaluation from the CNN prediction generation module 220, the overall CAD evaluation from the visual prediction generation module 225, and the overall CAD evaluation from the metadata prediction generation module 230.

[0087] The output from the prediction combination module 235 includes an intermediate, continuous (non-binary) and overall CAD evaluation (referred to as "intermediate CAD evaluation" in the following exemplary equation). Details regarding how the prediction combination module 235 may operate are provided below.

[0088] Z - score normalization is a statistical technique that centers the distribution around zero and scales the distribution so that the standard deviation is 1, and the output from this process is referred to as the "Z - score". As used in some embodiments, Z - score normalization ensures that evaluations (e.g., evaluations from 220, 225, 230) can be combined by an averaging operation and that when all evaluations are combined, one or more evaluations do not influence one or more other evaluations in an undesirable manner (e.g., dominate), i.e., are equivalent (e.g., each having the same mean and standard deviation) such that each evaluation is equally weighted and / or distributed. In other embodiments, Z - score normalization may be modified, used in different ways, used in connection with additional processing, not used at all, or may be entirely replaced by one or more other techniques, for example, to give desired preferential weighting and / or distribution to one or more evaluations as desired.

[0089] In one implementation, the "combined CNN evaluation" from component 220 (which in this example is the weighted sum of two individual CNN models, CNN model 129 and CNN model 85 already shown) is added to the output with respect to the overall CAD (metadata) evaluation of component / module 230 with a weighting of 1 to 3 (CNN to metadata) as shown in Equation 1. The resulting combined CNN evaluation (Equation 1) is then normalized by subtracting a pre - calculated constant representing the mean of the N = 411 stage I PSR test set ("N = 411_CNN_mean"), and this normalized value is divided by a second pre - calculated constant representing the standard deviation of the same set ("N = 411_CNN_std_dev"), and the result of performing these two operations is the Z - score for the combined CNN evaluation as shown in Equation 2.

Equation

[0090] In this implementation, the CAD evaluation output by the visual prediction generation module 225 is combined with the CAD evaluation output by the metadata prediction generation component 230. As a result, for women, the average value of these outputs is used (Equation 3), while for men, only the output of the metadata prediction generation module 230 is used (Equation 4). Then, the values of the "visual feature point evaluations" for women and men are combined, for example, through a set of union operators

Number

Number

[0091] Continuing with this exemplary implementation of the embodiment, next, an "intermediate CAD evaluation" value is created as the final output from the prediction combination module 235, as shown in Equation 7, by averaging the output from the CNN evaluation (i.e., the z-score according to Equation 2) and the output from the visual feature point evaluation (i.e., the z-score according to Equation 6). In other words, for each patient, module 235 sums the outputs of these z-scores and then divides the sum by 2.

Number

[0092] Continuing with this implementation of the embodiment, the CVMGD compensation module 240 receives both the subject's gender and / or age data 210 and the intermediate CAD evaluation output from the prediction combination module 235 as inputs and outputs a final and continuous (non-binary) CAD evaluation value that may be referred to as the "final and continuous CAD evaluation". Component 240 utilizes the concept of a cyclic variation score to calculate the maximum variation scores for ORTH1 and ORTH3 (and, in some embodiments, for ORTH2). This cyclic variation score obtains information regarding the changes between cardiac cycles and obtains electrophysiological variations that embed information content regarding CAD symptoms that are observed to be particularly applicable to male subjects within a given range of that score. The compensation operation implemented within module 240 improves the overall disease assessment (such as that provided through visual feature point assessment and machine learning assessment) by leveraging that cyclic variation information regarding male subjects. In fact, the CVMGD compensation module 240 operates based on the underlying rationale that, within a given range of cyclic variation, a positive CAD evaluation regarding a male subject when output from the prediction combination component 235 is likely to actually be a negative CAD evaluation, and conversely, a negative CAD evaluation generated by the prediction combination component 235 regarding a male subject is likely to be a positive CAD evaluation.

[0093] In some embodiments, the CVMGD compensation module 240 is configured to determine both (i) whether the periodicity score of a male subject (e.g., calculated by module 300) is within a pre-specified range (e.g., 0.0071 to 0.0079), and (ii) whether the intermediate CAD evaluation value for a given male subject, when output by module 235, is greater than or equal to an amount X when compared to a pre-specified “threshold value” (e.g., stored within module 240). When both conditions are met (e.g., in the case of a positive CAD evaluation for a male subject with a specific periodicity signature), the CVMGD compensation module 240 is configured to determine that the final and continuous CAD evaluation value for the male subject is output as the threshold value minus the amount X (i.e., providing a negative CAD evaluation), where X is determined as the threshold value minus the difference between the intermediate CAD evaluation score and the threshold value. That is, the CVMGD compensation module adjusts the intermediate CAD evaluation value determined via Equation 7 to a value equal to or of the same magnitude as the threshold value minus X. Conversely, the CVMGD compensation module 240 is configured to determine both (i) that the periodicity score of the male subject (stored within module 300) is within the same pre-specified range of the periodicity score, and (ii) that the intermediate CAD evaluation value is less than the value of an amount Y when compared to the same pre-specified threshold value. When both conditions are met (in the case of a negative CAD evaluation for a male subject with a specific periodicity signature), the CVMGD compensation module 240 is configured to determine the final and continuous CAD evaluation score as the threshold value plus the amount Y (i.e., providing a positive CAD evaluation), where Y is determined as the threshold value plus the difference between the threshold value and the intermediate CAD evaluation score. That is, the CVMGD module adjusts the intermediate CAD evaluation value to a value equal to or of the same magnitude as the threshold value plus Y. If the subject does not fall into any of the above scenarios, the score of the subject is not modified by module 240 and the original value passes through without being changed.When corrected, this change embeds the information that, within a predetermined range of the periodic variation as previously defined, a positive CAD assessment for a male (as the output from prediction combination component 235) is likely to be negative, and conversely, a negative CAD assessment for a male is likely to be positive.

[0094] In the example of module 240 for the embodiment of FIG. 2, it is assumed that there is a male subject having (i) an intermediate CAD assessment score of 0.14460082598168, and a periodic variation score (the maximum value of ORTH1 and ORTH3) within the pre-specified (periodic variation) thresholds of 0.0071 and 0.0079 (greater than 0.0071 and less than 0.0079), and (ii) a pre-specified (CAD assessment) threshold of 0.13460082598168. In this case, the CAD assessment is corrected by the compensation operation performed by module 240 such that an intermediate CAD assessment score of 0.124600825981680 is assigned to the subject (for example, calculated as the value obtained by subtracting the threshold 0.13460082598168 from the previous intermediate CAD assessment score of 0.14460082598168). As a result, the resulting final and continuous CAD assessment score is here made to be below the threshold of 0.13460082598168 by the amount of the difference between the threshold and the input intermediate CAD assessment value. Thus, the intermediate CAD assessment score (which was previously greater than the threshold) is here adjusted to be less than the threshold. And the corrected score is output from module 240. In fact, the positive CAD assessment generated by the prediction combination component 235 for the male subject is here output as a negative assessment.

[0095] The outputs of the signal quality assessment module 300 and the CVGMD compensation component 240 are variously used in subsequent end processing performed by various end blocks or modules / components, and the various end blocks or modules / components include, in the system 200 of FIG. 2, the final CAD prediction module 250, the localization prediction generation module 255, and the phase space volume dataset / image generation module 260 (illustrated as “PST generation” 260).

[0096] The final CAD prediction module 250 uses the final and continuous CAD evaluation from the CVMGD compensation component 240 and provides a final binary CAD evaluation as an output. In one implementation, if the final and continuous CAD evaluation is greater than or equal to a threshold value of 0.13460082598168, the subject is predicted to be CAD positive; otherwise, the subject is predicted to be CAD negative. The accuracy of the threshold value (e.g., 0.13460082598168) has 14 significant digits because, when generated in this example, the threshold value corresponds to the score belonging to a specific subject, and because it can be used to identify the threshold value source during analysis and progression. In other embodiments, other such threshold value values with various accuracies may be used.

[0097] The localization prediction generation module 255 uses, as inputs, the final binary CAD evaluation output from the final CAD prediction module 250 and the binary localization evaluation of the subject's coronary arteries (e.g., LCX, LAD, RCA), and outputs a CAD localization evaluation.

[0098] In some embodiments, the PST generation module 260 uses the uncorrected PSR signal 205 (via the signal quality assessment system 300) and outputs a PST dataset / image. In other embodiments, the PST generation module 260 uses the PSR signal preprocessed by other modules within the assessment system 110.

[0099] FIG. 3 is a diagram showing an exemplary signal quality evaluation system 300 according to an exemplary embodiment, and FIG. 4 is an operational flowchart relating to an implementation of a method 400 for evaluating signal quality according to another exemplary embodiment. The components of the signal quality evaluation system 300 are combined to create an effective methodology that enables subsequent analysis for signal quality evaluation. This signal quality evaluation method 400 can function as a gating stage for subsequent analysis.

[0100] System 300 includes a signal quality evaluator 305 and a measurement system 380. The signal quality evaluator 305 and the measurement system 380 may be provided within the same computing device, or, similar to all components of the systems described throughout this disclosure, may be provided within separate computing devices that communicate with each other (directly connected or coupled to each other, or communicatively connected or coupled to each other via a wired network or an optical network or a wireless network, etc.). The network may be a public switched telephone network (PSTN), a cellular / mobile phone network, a local area network (LAN) such as a wired or wireless Ethernet® network, a network including near field communication (NFC) or other radio frequency-based technologies and standards (e.g., Bluetooth®, Bluetooth® low energy, etc.), a packet-switched network (e.g., the Internet), etc., and may be of one or more various network types or may be composed of them. Although only one measurement system 380 is illustrated in FIG. 3, there is no limit to the number of measurement systems 380 that can be supported. The signal quality evaluator 305 and the measurement system 380, similar to all components of the systems described throughout this disclosure, may each be implemented using one or more processors associated with any of various computing devices such as a smartphone, a smartwatch, a desktop computer, a server computer, a mainframe computer, a laptop computer, a tablet computer, and a set-top box (including any combination thereof), etc. Other types of computing devices may be supported. A suitable computing device is illustrated as computing device 2200 in FIG. 22.

[0101] The measurement system 380 may be any measurement system such as measurement system 102, and the signal quality evaluator 305 may be implemented separately from the measurement system 102 or may be implemented within the measurement system 102.

[0102] The quality of any electrical signal, including biophysical signals such as PSR signals described herein (e.g., signals acquired at about 8 kHz), can be affected by noise resulting from various sources. Such noise can affect the signal quality acquired in various ways. For example, the noise can have an adverse effect on the performance of subsequent analysis, as described herein in relation to the clinical signs or disease symptoms of the subject or patient. Having an adverse effect on the performance of subsequent analysis can manifest in various ways. When the processing is performed remotely (e.g., within a cloud service), real-time or near-real-time rejection of the signal facilitates reacquisition or remeasurement of the patient, reducing the inconvenience and cost to the patient of having to return, for example, to a clinic, hospital, or other healthcare site for signal reacquisition. Further, when subsequent analysis involves the generation of a dataset / image (e.g., a phase space dataset / image) for interpretation by a physician, the image may not accurately represent the physiological state of the subject, potentially leading to misinterpretation and, consequently, delayed or incorrect diagnosis and / or treatment. When subsequent analysis involves an explicit quantitative assessment of a given disease state of the subject, a negative execution can involve an incorrect quantification of the disease state, resulting in delayed or incorrect withholding of treatment, or potentially unnecessary additional tests or interventions, which can be harmful to the patient. Further, regardless of the type of analysis, noise can increase the processing time and / or cost (in computing resources) to generate an output. Thus, it can be useful to identify and quantify the noise, whereby it may be removed, minimized, or otherwise processed, either wholly or partially (or even, in some cases, together with one or more associated signals), thereby eliminating or minimizing such adverse effects. Examples of noise relevant to the present disclosure include, for example, power line interference, high-frequency noise, high-frequency noise bursts, (abrupt) baseline shifts, and periodic variations.For the purposes of the present disclosure, any unwanted interference within the signals disclosed herein may be considered "noise", regardless of its source.

[0103] As can be understood from the exemplary methods shown in FIGS. 3 and 4, in signal acquisition step 410, measurement system 380 acquires an input signal 383, such as PSR signal 205, and provides it to signal quality evaluator 305. The input signal 383 may be a biophysical signal or a biophysical signal dataset acquired with respect to a subject. In the case of a cardiac signal, by using a handheld device or other device, for example, from a single probe / sensor or from a set of any number of probes / sensors or electrodes (e.g., six probes 114a - 114f), for example, in the case of six probes, as described above, resting chest physiological signals of the subject may be collected from those arranged along three orthogonal axes corresponding to the ORTH1, ORTH2, and ORTH3 channels. Electrodes as part of the non-invasive measurement system 102 can acquire phase gradient biophysical signals (as derived from set 108 as described above) without using ionizing radiation, contrast agents, motion, or pharmacological stressors, although in some embodiments, nevertheless, the biophysical signals of interest can be used in combination with such protocols or devices. The non-invasive measurement system 102 samples at approximately 8 kHz over a duration of from about 30 seconds to about 1400 seconds, preferably over a duration of about 210 seconds, in some embodiments. The acquired data points are transferred to evaluation system 110 as part of dataset 108 and evaluated, for example, by an internal analysis engine employing a machine-learned algorithm / predictor. Other electrode sets and electrographic acquisition methodologies may be used when applying the methods and systems disclosed herein.

[0104] As further described herein, in step 420, the signal quality evaluator 305 performs quality evaluation and generates an output such as output 386 by using one or more of, or any of, or all of, the power line interference module 320, the high frequency noise module 330, the high frequency noise burst module 340, the sudden movement module 350, and the periodic variation noise module 360, in combination with the determination module 310. The signal quality evaluator 305 and / or one or more of the power line interference module 320, the high frequency noise module 330, the high frequency noise burst module 340, the sudden movement module 350, the periodic variation noise module 360, and / or the determination module 310 may be resident on or within a device or apparatus that houses the measurement system 102 or the signal quality evaluation system 300 or includes them in some other manner, or may be arranged in some other way, or may be arranged locally and remotely (e.g., a server / processor, software, and services, etc. that exist and / or operate within the "cloud" and communicate with a local server / processor via a network such as the Internet), or may be a hybrid system / hybrid configuration in which part of the quality evaluation operation is performed on a device and part of the quality evaluation operation is performed remotely. An example of a suitable device or apparatus is illustrated as the computing device 2200 in FIG. 22.

[0105] In step 430, the output 386 is provided to the measurement system 380 and the user. In one implementation, the output 386 includes an indicator for accepting or rejecting one or more acquired signals for subsequent processing and analysis (e.g., performed by the intermediate processing components and the end processing components described with respect to FIG. 2).

[0106] In one implementation, the input signal 383 is the unmodified PSR signal (channel data from ORTH1, ORTH2, and ORTH3, parsed from a PSR file downloaded from the PSDR (Phase Signal Data Repository)), which, as described above, corresponds to a series of bipolar acquisition channel data recorded by a phase space recorder from electrodes 114 arranged along or in the vicinity of one orthogonal axis passing through the subject's body from the left side to the right side of the subject. Also, the data from channel "ORTH2" corresponds to a second series of bipolar acquisition channel data recorded by a phase space recorder from two other electrodes 114 arranged along or in the vicinity of a second orthogonal axis passing through the subject's body from the upper side to the lower side of the subject. And the data from channel "ORTH3" corresponds to a third series of bipolar acquisition channel data recorded by a phase space recorder from two further electrodes 114 arranged along or in the vicinity of a third orthogonal axis passing through the subject's body from the front to the back of the subject. The signals of ORTH1, ORTH2, and ORTH3, and the corresponding data sets can be arranged, for example, within mutually orthogonal axes in phase space coordinates.

[0107] In one implementation, the output 386 includes an assessment of whether to proceed with further analysis and a time window of the signal suitable for analysis, which is ultimately used to determine whether the patient is considered CAD positive.

[0108] As further described with reference to FIGS. 3 and 5-7, the power line interference module 320 detects or otherwise determines power line interference noise (e.g., noise caused by a 60 Hz power line (a frequency commonly used in parts of Canada, Korea, Taiwan, the United States, and Japan, etc.) and its harmonics, and noise caused by a 50 Hz power line (a frequency commonly used in parts of China, France, Germany, Hong Kong, India, Italy, Switzerland, the United Kingdom, and Japan, etc.) and its harmonics). The determined power line interference noise is provided to the determination module 310 for processing such as processing further described herein.

[0109] As further described with reference to FIGS. 3 and 8-10, the high frequency noise module 330 detects or otherwise determines excessive signal frequency components (e.g., in one implementation, frequency components greater than 170 Hz, such frequency components above 170 Hz are not necessarily periodic and may include pulses and other such artifacts). The output of the high frequency noise module 330 is provided to the determination module 310 for processing such as processing further described herein.

[0110] As further described with reference to FIGS. 3 and 11-13, the high frequency noise burst module 340 detects or otherwise determines short bursts of the above-mentioned excessive high frequency components. The output of the high frequency noise burst module 340 is provided to the determination module 310 for processing such as processing further described herein.

[0111] As further described with reference to FIGS. 3 and 14-16, the abrupt movement module 350 detects or otherwise determines extreme baseline fluctuations localized in a particular segment of the signal, fluctuations large enough to cause signal distortion. The output of the abrupt movement module 350 is provided to the determination module 310 for processing such as processing further described herein.

[0112] As further described with respect to FIGS. 3 and 17-19, the periodic variation noise module 360 provides quantification of noise asynchronous to the cardiac cycle, which may include the potential generated by the activation of skeletal muscle. The output of the periodic variation noise module 360 is provided to the decision module 310 for processing, such as processing further described herein. The concepts described herein may be applied to quantify noise asynchronous to other periodic physiological signals outside of the cardiac situation.

[0113] In one implementation, the evaluation described herein is performed at the channel level, e.g., the ORTH1 channel, the ORTH2 channel, the ORTH3 channel, are evaluated independently above and combined at a subsequent stage of processing.

[0114] FIG. 5 is an operational flow diagram relating to an implementation of a method 500 for evaluating power line interference according to another exemplary embodiment. In one embodiment, the power line coefficient can be an indicator of the contamination of power line noise, with larger values indicating greater contamination.

[0115] In step 510, the power line coefficient is calculated by switching from the time domain to the frequency domain using the Fast Fourier Transform (FFT). In step 520, the presence of a deviation or peak in the FFT periodogram is quantified, for example, at 60 Hz. In some embodiments, this method determines the base local frequency energy through the average decibel values at 55 Hz to 58 Hz and 62 Hz to 65 Hz (e.g., base = absolute value (average (power (55 ≤ frequency ≤ 58 or 62 ≤ frequency ≤ 65)))), thereby providing a baseline power for detecting whether there is a peak occurring at the center of that range (e.g., 58 Hz to 62 Hz). Thereafter, the method determines the maximum value of the power line energy through the maximum decibel value at 58 Hz to 62 Hz (e.g., peak height = base - absolute value (average (power (58 ≤ frequency ≤ 62)))), thereby quantifying the peak at 58 Hz to 62 Hz. Thereafter, the method determines the ratio of the height of the peak above the base when compared to the base. That is, the following is determined.

Number

[0116] In step 530, the power line coefficient is further extended by including, for 180 Hz and 300 Hz, the addition with a small weighting of the harmonics (from 60 Hz), calculated using the same methodology as described above for the power line coefficient in the case of 60 Hz. Depending on the implementation, it is assumed that other frequencies and harmonics may be used. The output from this evaluation is the power line coefficient for each of the ORTH1 channel, the ORTH2 channel, and the ORTH3 channel, having the basic name "power line coefficient" (modified only to indicate the data channel). In step 540, it is determined whether to reject the input signal based on power line interference by determining the output (i.e., the power line coefficient) as a score to be compared with a threshold. In some embodiments, the power line coefficient score is determined as follows.

Number

[0117] In this specification, drawings (plots) are used (e.g., FIGS. 6, 7, 9, 10, 12, 13, 15, 16, 18, 19) to assist in the description of different types of noise and to share common types of labels. Time is along the horizontal axis (time points in units of samples), where it is reflected as the number of data points in an 8 kHz signal (8000 samples / second are acquired), and it should be noted that the amplitude is along the vertical axis. In these figures, although the amplitude is shown in mV, in some implementations, a normalized amplitude obtained by subtracting the mean of the signal and dividing by the standard deviation may be used (thus converting the signal to a z-score, e.g., to the number of standard deviations from the mean value of the data points, and the sign indicates the direction).

[0118] For example, FIG. 6 is a diagram 600 showing an observable characteristic, such as the maximum power line interference characteristic, in the heart situation of the power line interference evaluation operation of FIG. 5 according to another exemplary embodiment. FIG. 6 shows the maximum power line coefficient at the channel level. The power line interference can be clearly seen between ventricular depolarization signals as periodic oscillations in the amplitude range of -9.15 mV to -9.45 mV.

[0119] FIG. 7 is a diagram 700 showing an observable characteristic, such as the power line interference at a threshold, in the method of the power line interference evaluation operation of FIG. 5 according to an exemplary embodiment. FIG. 7 shows a narrowly accepted power line coefficient, e.g., showing a first coefficient that would be accepted, and those greater than this first coefficient are rejected. That is, in this data, it was found that the power line coefficient for 60 Hz was accepted, but the power line coefficients for 180 Hz and 300 Hz were not accepted.

[0120] FIG. 8 is an operational flow diagram related to the implementation of an exemplary method 800 for evaluating high-frequency noise according to an exemplary embodiment. The high-frequency noise score is calculated in 810 by first performing a stationary wavelet transform (SWT). Note that this is functionally similar to the fast Fourier transform, except that the SWT enables frequency localization in time at the expense of frequency detail. In step 820, the energy at levels 9 to 13 corresponding to components greater than 170 Hz is saved, and in step 830, the inverse SWT is performed. The result of these transformations is the high-frequency component returned to the time domain (which would become visible if the intermediate output were plotted).

[0121] In step 840, the signal-to-noise ratio (SNR) is calculated as the high-frequency noise score through a comparison between the original signal and the noise extracted by the SWT in the time domain. The output from this evaluation has the basic name "high-frequency noise-to-signal ratio", which is modified only to indicate the source channel.

[0122] In step 850, it is determined whether to reject the input signal based on high-frequency noise by comparing the output (i.e., the high-frequency noise score) with a threshold. In one implementation, the signal is rejected based on high-frequency noise if the high-frequency noise score is greater than 0.05273.

[0123] FIG. 9 is a diagram 900 showing an observable characteristic, namely the maximum high-frequency noise in a cardiac situation, in the high-frequency noise evaluation operation of FIG. 8, for example, according to an exemplary embodiment. Here, the high-frequency noise can be seen as the thick and dark line partially surrounded by box 902 in FIG. 9, for example. This noise in FIG. 9 starts in the amplitude range of about 3 mV to about 4 mV and decreases over time with a slow baseline fluctuation down to about -2 mV to about -4 mV.

[0124] FIG. 10 is a diagram 1000 showing high-frequency noise at a threshold related to the high-frequency noise evaluation operation of FIG. 8 according to an exemplary embodiment in the context of the heart condition. Here, the signal is just acceptable. The time scale on the x-axis is smaller than that of FIG. 9, and the phase waveforms are clearly distinguishable (e.g., QRS waveform, T waveform, etc.). In this particular example, the high-frequency noise can be seen as an impulse between the offset of ventricular repolarization and the atrial depolarization event. The noise is most prominent along the entire baseline after ventricular repolarization (e.g., the "T wave") and before atrial depolarization ("P wave"), as shown, for example, in box 1010. Such noise occurs in all cycles.

[0125] FIG. 11 is an operational flowchart related to an exemplary implementation of a method 1100 for evaluating high-frequency noise bursts. The score obtained from method 1100 quantifies the same high-frequency noise as described with respect to FIG. 8, but instead is localized to 1-second segments. The value of this score, along with the sudden or extreme baseline fluctuations (further described herein), by itself may not be able to remove the signal from subsequent analysis, but will occur when the signal length is insufficient (e.g., at least continuously for 16 seconds and the same period across all channels) without high-frequency noise bursts or sudden baseline shifts occurring.

[0126] The calculation of this score related to this exemplary method 1100 is similar to the overall high-frequency score calculation described elsewhere in this specification. For example, in steps 1110, 1120, and 1130, the same or similar methods as described with respect to FIG. 8 (steps similar to 810, 820, and 830, respectively) are used to extract the high-frequency components from the signal.

[0127] In step 1140, the illustrated exemplary process uses this high-frequency time-series data obtained in relation to step 1130 to test a 1-second window and compares the result with the overall median high-frequency energy calculated over all signals. In step 1150, in one implementation, if the energy obtained in a given 1-second window, or a set of given 1-second windows, is greater than twice the overall median, that segment is flagged as containing high-frequency noise. In one implementation, any single 1-second window can be flagged. When flagging a set of 1-second windows, all must meet the same criteria as a 1-second window that is twice the overall median energy. Thus, the criteria are the same for a set of windows as for a single window.

[0128] In one implementation, the output from this evaluation is two scores that are modified to indicate the source data channels. For example, the scores may be output labeled as "Median High-Frequency Signal Energy" (indicating the median high-frequency energy overall) and "High-Frequency Noise Burst Coefficient" (indicating the 1-second energy of any window greater than four times the median high-frequency signal energy). If there are multiple such windows, the maximum value is returned.

[0129] FIG. 12 is FIG. 1200 showing an observable characteristic, the maximum high-frequency noise burst 1210, in, for example, the high-frequency noise burst evaluation operation of FIG. 11, according to an exemplary embodiment. In this case, the high-frequency noise burst 1210 is characterized as an impulse that persists over several data points (e.g., less than 1 millisecond). As with other figures (FIGS. 6, 7, 9, 10, 13, 15, 16, 18, 19), the units on the x-axis are samples and the units on the y-axis are mV (not normalized).

[0130] FIG. 13 is a diagram showing another observable characteristic of the high-frequency noise burst 1300 in, for example, the high-frequency noise burst evaluation operation of FIG. 11, according to an exemplary embodiment. This is a more typical characteristic regarding high-frequency noise bursts than that illustrated in FIG. 12, and in this figure, there are a plurality of high-frequency bursts indicated by 1310 that are observed over the illustrated time frame.

[0131] FIG. 14 is an operational flowchart regarding the implementation of a method 1400 for evaluating a sudden movement of a baseline, according to an exemplary embodiment in a cardiac situation. The movement of the baseline is defined as "sudden" in this example when the baseline of the 1-second window of the signal changes by more than 25% of the ventricular depolarization amplitude of the channel (compared to the baseline of the previous window). Other definitions regarding "sudden" in this context may be established depending on the type of biophysical signal acquired and analyzed, the physiological state of the patient, and other factors as necessary.

[0132] In step 1410, a baseline within a 1-second window of the input signal is determined. In step 1420, a baseline within the next 1-second window of the signal is determined. In step 1430, a score is determined based on the baseline movement between the two windows.

[0133] In one implementation, a score of zero is assigned if the movement within the window is less than 25%. If there are multiple movements greater than 25% within the 1-second window, the maximum value of those movements is assigned to that window. The output from this evaluation has the basic name "maximum sudden movement percentage", and this basic name is modified only to indicate the source channel.

[0134] FIG. 15 is an exemplary diagram 1500 showing an observable characteristic of the maximum abrupt movement for a subject, for example, in the abrupt baseline movement evaluation operation of FIG. 14, according to an exemplary embodiment in the context of the heart. Here, the phase signal is initially stable with an amplitude of 100 mV, but subsequently, first, the value of this baseline decreases by about 20 mV, and then significantly decreases to around 30 mV. During these abrupt baseline changes, the phase signal severely decreases or does not exist.

[0135] FIG. 16 is an exemplary diagram 1600 showing an observable characteristic of an abrupt movement of about 50%, for example, in the abrupt baseline movement evaluation operation of FIG. 14, according to an exemplary embodiment in the context of the heart. This is a more typical example regarding abrupt baseline movement, where it is illustrated as 1610 here. 1610 illustrates an upward movement regarding the signal amplitude and a subsequent downward movement of the same degree. Due to this movement of the signal amplitude, the ventricular depolarization event at the top of the movement will have an amplitude approximately 50% greater than the ventricular depolarization event before its 1 - second window.

[0136] FIG. 17 is an operational flowchart regarding the implementation of a method 1700 for evaluating periodic variations, according to an exemplary embodiment in the context of the heart. Examination of phase periodic variations (in the cardiac system, also in other physiological systems, or in combinations thereof) reveals asynchronous noise, which can quantify the presence of muscle noise artifacts and other types of noise lacking alignment with the phase period.

[0137] The periodic variation noise can be calculated using the exemplary technique of FIG. 17, which detects components within the frequency range band of the phase signal and having similar amplitudes.

[0138] In step 1710, all phase periods within the selected channel are detected by flagging each ventricular depolarization event (for example, the time point during each phase period when the electrical activity of the ventricle is maximum).

[0139] In step 1720, a template phase period representing all detected phase periods is created. In step 1730, each detected phase period is compared with the template, and the difference is quantified.

[0140] In step 1740, in this technique, the final periodic variation score for the channel is created by compressing all of the differences resulting over the detected phase periods.

[0141] In one implementation, the final periodic variation score is calculated for each of channels ORTH1 and ORTH3, and the larger of these two scores is used to generate the overall score for the signal. In this example, ORTH2 has been excluded from this calculation because it was found to have a maximum final periodic variation score that was much larger than one-third of the time over two-thirds of the time compared to ORTH1 and ORTH3 (which would be statistically expected if each of the three channels had an equal likelihood of having the maximum final periodic variation). Thus, including the final periodic variation score for the signal from the ORTH2 channel would unbalance or skew the value of the overall score compared to the contributions from the other two channels. However, in an alternative implementation, the maximum value may be calculated from all three channels (ORTH1, ORTH2, and ORTH3). Although this alternative maximum value is expected to be larger than the value based on ORTH1 and ORTH3, this technique makes the criteria for signal quality more stringent, which may be useful in certain situations.

[0142] In a human subject in a cardiac situation, the ORTH2 vector typically extends from just below the subject's left clavicle to just below the end of the rib cage, and without being bound by theory, there can be two problems related to signal acquisition that can account for a relatively large final periodic variation score, and thus a relatively large associated noise level, compared to other channels. First, the end of the ORTH2 vector just below the rib cage is located over the stomach, and subjects to be evaluated for CAD tend to have a higher body mass index (BMI) compared to the general population (BMI is a risk factor for CAD). A high BMI typically indicates an excessive amount of abdominal fat that changes the impedance between two electrodes typically used for signal acquisition. Second, the ORTH2 vector is perpendicular to the striated muscle of the left pectoralis muscle, increasing the likelihood that noise associated with the pectoralis muscle will enter and / or affect the signal. Muscle noise is generated by contraction, but contraction does not necessarily mean movement. For example, an isometric contraction is a static contraction of a muscle where there is no visible movement in the joint angle.

[0143] FIG. 18 is FIG. 1800 showing an observable characteristic of maximum periodic variation noise, such as in the periodic variation evaluation operation of FIG. 17, according to an exemplary embodiment in a cardiac situation. The noise can be most seen between phase periods, during a period after the offset of the ventricular repolarization event and before the atrial depolarization event, and appears similar to an external phase waveform (one or more additional ventricular repolarization events, etc.). This example of noise is shown by box 1810. Note that the noise occurs in all periods and is most visible between the subject's heartbeats (e.g., the QRS waveform is shown as a "spike").

[0144] FIG. 19 is FIG. 1900 showing an observable characteristic of a smaller periodic variation noise, e.g., in the periodic variation evaluation operation of FIG. 17, according to an exemplary embodiment in the context of the heart condition. This figure shows a lower level of noise contamination as compared to, e.g., the noise signature of FIG. 18. Other differences can also be seen here, e.g., the noise has a relatively low frequency and a relatively large amplitude range, and the latter results in a more impulse-like signature in the example of FIG. 19.

[0145] FIG. 20 is an operational flowchart relating to the implementation of a method 2000 for evaluating signal quality according to an exemplary embodiment. In particular, in the signal user utility workflow described with respect to FIG. 20, a score is evaluated.

[0146] In step 2005, an input signal is received (e.g., PSR signal 205).

[0147] In step 2010, a power line interference test is performed to determine whether the power line coefficient of the input signal is greater than a threshold. If so, in step 2060, the input signal is rejected. Otherwise, the input signal is considered to have passed this test, and in this example, a binary number indicating the passed test #1 is provided to the Boolean "logical AND" operator.

[0148] In step 2015, another high-frequency noise test (test #2) is performed to determine whether the high-frequency noise-to-signal ratio is greater than a threshold. If so, in step 2060, the input signal is rejected. Otherwise, the input signal is considered to have passed this test #2, and in this example, a binary number indicating the passed test #2 is provided to the Boolean "logical AND" operator.

[0149] In steps 2020, 2025, and 2030, a burst movement test (collectively, "Test #3") is performed to determine whether a clean signal segment is available for burst movement and high-frequency noise bursts. If it is shown that no such clean signal segment is available for any one or more of the partial tests of Test #3 in each of steps 2020, 2025, and 2030, then in step 2060, the input signal is rejected. Otherwise, the input signal is considered to have passed each test in steps 2020, 2025, and 2030, and the binary number indicating that the input signal is Test #3 and has passed the test is provided to the Boolean "logical AND" operator.

[0150] In step 2030, a periodic variation test (Test #4) is performed on the input signal to determine whether there is periodic variation noise greater than a threshold (e.g., on ORTH1 or on ORTH3). If so, in step 2060, the input signal is rejected. Otherwise, the input signal is considered to have passed this Test #4, and the binary number indicating that Test #4 has passed is provided to the Boolean "logical AND" operator.

[0151] After the test is completed and the above binary numbers are provided to the Boolean "logical AND" operator, this operator in step 2040 then performs a Boolean operation based on these inputs, and if all input binary numbers indicate that the test has been passed, then in step 2070, the input signal is accepted. This logic stipulates that for a signal to pass and be accepted, it must pass all tests (step 2070). If it fails any test, the signal is rejected (step 2060).

[0152] Therefore, for any given signal, (1) if any channel of the patient or subject fails the power line interference reference test #1 in step 2010, the input signal of the patient is rejected; (2) if any channel of the subject fails the high-frequency noise standard, the input signal of the subject is rejected; (3) if the patient's channels ORTH1, ORTH2, and / or ORTH3 fail the periodic variation threshold, the input signal of the patient is rejected; (4) if a sudden movement or high-frequency noise burst is detected in the patient's input signal, an attempt is made to find at least one 16-second segment of the signal in which no sudden movement or noise burst is detected, and it is verified whether such a segment is the same across all channels (e.g., all channels are clean during that segment), and if no such window is found, the input signal of the patient is rejected, and if otherwise (i.e., if such a window is found), the input signal of the patient is processed using the discovered window; (5) when all channels of the patient's input signal pass all the standards, the input signal of the subject is processed normally, which is shown as the "Boolean logical product" operator 2040 in FIG. 20 (i.e., all tests must be passed).

[0153] An exemplary table for the tests and thresholds in one implementation is shown in Table 1.

Table 1

[0154] In North America, for example, regarding the detection of the power line frequency, typically 60 Hz is used. However, in other regions such as China, the EU, India, etc., typically a power line frequency of 50 Hz rather than 60 Hz is used, so a correction of the score for that specific frequency may be desired.

[0155] Figures 21A and 21B show the architecture and data flow diagram 2100 for exemplary signal quality evaluation components according to an exemplary embodiment.

[0156] Failure of any test, in the exemplary example, does not prevent subsequent execution of the evaluation system 200. Rather, the failure may be reported through the following mechanism. Note that the PST evaluation is attempted.

[0157] The input may include uncorrected ORTH1 data, ORTH2 data, ORTH3 data, parsed from the phase space recorder file.

[0158] The output may be passed to data transfer APIs (DTAPI) and a report database (RD). In one implementation, legacy fields "noise volume" and "noise level average value" are reused to maintain backward compatibility within the DTAPI component and the RD component. In some embodiments, the report database is configured to store the noise volume parameter as a periodic variation score if all other signal quality evaluation tests have passed. If it fails any test (other than periodic variation), a flag of 10000 is used to indicate that state. The periodic variation may have a threshold of 0.0106 in one implementation.

[0159] For example, in some embodiments, the noise level average value stores a status code having the following possible states as provided in Table 2.

Table 2-1

Table 2-2

[0160] FIG. 22 illustrates an exemplary computing environment in which exemplary embodiments and aspects according to the exemplary embodiments can be implemented, such as evaluation system 110 and signal quality evaluator 305. 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.

[0161] A number of other general-purpose or special-purpose computing device environments or configurations may be used. Examples of well-known computing devices and / or environments and / or configurations suitable for use include, but are not limited to, personal computers, server computers, handheld devices or laptop devices, multiprocessor systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, distributed computing environments including any of the above systems or devices, and the like.

[0162] Computer-executable instructions, such as program modules, may be used. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. A distributed computing environment may be used in which tasks are performed by remote processing devices linked via 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.

[0163] Referring to FIG. 22, an exemplary system for implementing the aspects described herein includes a computing device such as computing device 2200. In its most basic configuration, computing device 2200 typically includes at least one processing unit 2202 and a memory 2204. Depending on the detailed configuration and type of the computing device, memory 2204 may be volatile (such as random access memory (RAM)), or non-volatile (such as read only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated by dashed line 2206 in FIG. 22.

[0164] Computing device 2200 may have additional feature points / functions. For example, computing device 2200 may include additional storage (removable and / or non-removable) including, but not limited to, magnetic disks, optical disks, or tapes. Such additional storage is illustrated in FIG. 22 by removable storage 2208 and non-removable storage 2210.

[0165] Computing device 2200 typically includes various computer-readable media. Computer-readable media can be any available media that can be accessed by device 2200 and includes both volatile and non-volatile media, as well as removable and non-removable media.

[0166] A computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. All of memory 2204, removable storage 2208, and non-removable storage 2210 are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage, or any other medium that can be used to store desired information and can be accessed by computing device 2200. Any such computer storage media may be part of computing device 2200.

[0167] Computing device 2200 may include one or more communication connections 2212 that enable the device to communicate with other devices. Computing device 2200 may also have one or more input devices 2214, such as a keyboard, mouse, pen, voice input device, touch input device, etc., alone or in combination. Computing device 2200 may also include one or more output devices 2216, such as a display, speaker, printer, vibration mechanism, etc., alone or in combination. All of these devices are well known in the art and need not be described in detail here.

[0168] It will be understood that the various techniques described herein may be implemented in relation to hardware components or software components, or in appropriate cases, combinations thereof. Exemplary types of hardware components that may be used include graphics processing units (GPUs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on chip systems (SOCs), complex programmable logic devices (CPLDs), and the like. The methods and apparatuses, or specific aspects or portions thereof, relating to the subject matter of the present disclosure may also take the form of program code (i.e., instructions) embodied in a tangible medium such as a floppy disk, CD-ROM, hard drive, or any other machine-readable storage medium such that when the program code is read and executed by a machine such as a computer, the machine becomes an apparatus for practicing the subject matter of the present disclosure.

[0169] Although exemplary implementations may refer to utilizing aspects relating to the subject matter of the present disclosure in the context of one or more stand-alone computer systems, the subject matter is not so limited, but rather may be implemented in relation to any computing environment such as a network or distributed computing environment. Furthermore, aspects relating to the subject matter of the present disclosure may be implemented on or across multiple processing chips or devices, and storage may similarly have an effect across multiple devices. Such devices may include, for example, personal computers, network servers, handheld devices, and wearable devices.

[0170] Although the subject matter has been described in language specific to structural features and / or methodological acts, it will 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 exemplary forms for implementing the claims.

[0171] Further examples regarding various processes that can be used in conjunction with the illustrated methods and systems are described in U.S. Patent No. 9,289,150 entitled "Non-invasive Method and System for Characterizing Cardiovascular Systems", U.S. Patent No. 9,655,536 entitled "Non-invasive Method and System for Characterizing Cardiovascular Systems", U.S. Patent No. 9,968,275 entitled "Non-invasive Method and System for Characterizing Cardiovascular Systems", U.S. Patent No. 8,923,958 entitled "System and Method for Evaluating an Electrophysiological Signal", U.S. Patent 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. Patent 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. Patent No. 9,737,229 entitled "Noninvasive Electrocardiographic Method for Estimating Mammalian Cardiac Chamber Size and Mechanical Function", U.S. Patent No. 10,039,468 entitled "Noninvasive Electrocardiographic Method for Estimating Mammalian Cardiac Chamber Size and Mechanical Function", "NoninvasiveU.S. Patent No. 9,597,021 entitled "Method for Estimating Glucose, Glycosylated Hemoglobin and Other Blood Constituents", U.S. Patent No. 9,968,265 entitled "Method and System for Characterizing Cardiovascular Systems From Single Channel Data", U.S. Patent No. 9,910,964 entitled "Methods and Systems Using Mathematical Analysis and Machine Learning to Diagnose Disease", U.S. Patent Application Publication No. 2017 / 0119272 entitled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition", PCT International Application Publication No. WO2017 / 033164 entitled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition", U.S. Patent Application Publication No. 2018 / 0000371 entitled "Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation", PCT International Application Publication No. WO2017 / 221221 entitled "Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation", U.S. Patent No. 10,292,596 entitled "Method and System for Visualization of Heart Tissue at Risk", "Method and System for Wide-bandU.S. Patent Application Publication No. 2018 / 0249960 entitled "Phase Gradient Signal Acquisition", U.S. Patent Application Publication No. 2019 / 0214137 entitled "Method and System to Assess Disease Using Phase Space Volumetric Objects" filed on December 26, 2018, PCT International Application Publication No. IB / 2018 / 060708 entitled "Method and System to Assess Disease Using Phase Space Volumetric Objects", U.S. Patent Application Publication No. US2019 / 0117164 entitled "Method and Systems of De-Noising Magnetic-Field Based Sensor Data of Electrophysiological Signals", U.S. Patent Application Publication No. 2019 / 0214137 entitled "Method and System to Assess Disease Using Phase Space Tomography and Machine Learning" filed on December 26, 2018, PCT International Application Publication No. PCT / IB2018 / 060709 entitled "Method and System to Assess Disease Using Phase Space Tomography and Machine Learning", U.S. Patent Application Publication No. 2019 / 0384757 entitled "Method and Systems to Quantify and Remove Asynchronous Noise in Biophysical Signals" filed on June 18, 2019, U.S. Patent Application Publication No. 2019 / 0365265 entitled "Method and System to Assess Pulmonary Hypertension Using Phase Space Tomography and Machine Learning", "Method and System to Assess Disease filed simultaneously with this specificationU.S. Patent Application No. __ / __ / __ entitled "Using Phase Space Tomography and Machine Learning" (having docket number 10321-034us1 and claiming priority from U.S. Provisional Patent Application No. 62 / 784,984 and U.S. Provisional Patent Application No. 62 / 835,869), U.S. Patent Application No. __ / __ / __ entitled "Method and System to Configure and Use Neural Network To Assess Medical Disease" (having docket number 10321-037pv1 and claiming priority from U.S. Provisional Patent Application No. 62 / 784,925), U.S. Patent Application No. 15 / 653,433 entitled "Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions", U.S. Patent Application No. 15 / 653,431 entitled "Discovering Genomes to Use in Machine Learning Techniques", U.S. Patent Application No. __ / __ / __ entitled "Method and System to Assess Disease Using Dynamical Analysis of Cardiac and Photoplethysmographic Signals" (having docket number 10321-041pv1 and claiming priority of the application), U.S. Patent Application No. __ / __ / __ entitled "Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals" (having docket number 10321-040pv1 and claiming priority of the application), each of these patent documents is hereby incorporated by reference in its entirety into this specification.

[0172] Unless otherwise expressly stated, it is not intended that any method described herein be construed as requiring that its steps be performed in a particular order. Accordingly, where a claim for a method does not actually recite an order to be followed by its steps, or where the steps are not otherwise specifically recited in the claim or description as being limited to a particular order, it is not intended that any order be inferred in any respect. This holds true for any possible implicit basis for interpretation, including logical matters regarding step composition or operation flow, plain meaning derived from grammatical construction or punctuation, or the number or type of embodiments described in the specification.

[0173] Although the methods and systems have been described in connection with specific embodiments and specific examples, it is intended that the embodiments herein be illustrative rather than limiting in all respects, and thus it is not intended to limit the scope to the specific embodiments described.

[0174] The methods, systems, and processes described herein may be used in connection with procedures such as the placement of a vascular stent into a blood vessel, such as an artery of a living (e.g., human) subject, and for generating stenosis and FFR outputs, as well as for use in connection with other invasive and surgical systems or processes. In one embodiment, the methods, systems, and processes described herein may be configured to determine and / or modify, by using the FFR / stenosis output, the number of stents to be placed within a living body (e.g., human), including, among other things, the optimal location to be placed within a given blood vessel, during an intra-operative procedure.

[0175] Examples of other biophysical signals that can be analyzed, in whole or in part, using the exemplary methods and systems include, but are not limited to, electrocardiogram (ECG) datasets, electroencephalogram (EEG) datasets, gamma synchronization signal datasets, respiratory function signal datasets, pulse oximetry signal datasets, perfusion data signal datasets, quasi-periodic biological signal datasets, fetal ECG datasets, blood pressure signals, cardiac magnetic field datasets, and heart rate signal datasets.

[0176] The exemplary analysis can be used for the diagnosis and treatment of heart-related conditions and symptoms and / or nerve-related conditions and symptoms, and such an evaluation can be applied to the diagnosis and treatment (including surgical, minimally invasive, and / or pharmacological treatments) of any condition or symptom in which a biophysical signal is involved in any relevant system of the living body. An example in the context of the heart is to perform, either alone or in combination, the diagnosis of CAD and any number of treatment methods such as stent placement in the coronary artery, performing atherectomy, performing angioplasty, prescribing drug therapy, and / or prescribing exercise, nutrition, and other lifestyle changes. Other heart-related conditions or symptoms that can be diagnosed include, for example, arrhythmia, congestive heart failure, valvular insufficiency, 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 thrombus, and pulmonary hypertension due to other diseases or other disorders such as blood), and other heart-related conditions, symptoms, and / or diseases. Non-limiting examples of nerve-related diseases, conditions, or symptoms that can be diagnosed include, for example, epilepsy, schizophrenia, Parkinson's disease, Alzheimer's disease (and all other forms of dementia), autism spectrum (including Asperger's syndrome), attention deficit hyperactivity disorder, Huntington's disease, muscular dystrophy, depression, bipolar disorder, brain / spinal cord tumors (malignant and benign), movement disorders, cognitive impairments, speech impairments, various psychoses, brain / spinal cord / nerve injuries, chronic traumatic encephalopathy, cluster headache, migraine, neuropathy (various forms including peripheral neuropathy), phantom limb / phantom limb pain, chronic fatigue syndrome, acute and chronic pain (including low back pain, failed back surgery syndrome, etc.), movement disorders, anxiety disorders, symptoms caused by infections or foreign substances (e.g., Lyme disease, encephalitis, rabies), narcolepsy and other sleep disorders, post-traumatic stress disorder, nerve symptoms / effects related to stroke, aneurysm, hemorrhagic injury, etc., tinnitus and other hearing-related diseases / symptoms, and vision-related diseases / symptoms.

Claims

[Claim 1] The invention described in this specification.

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