Method and system for automated quantification of signal quality

Abstract

Systems and methods are provided for quantifying the quality of acquired signals for evaluation and for gating acquired signals for subsequent analysis. Signals are acquired and a real-time determination is made as to whether there are any issues with the acquisition (e.g., whether the acquired signal is acceptable or unacceptable and of sufficient quality for subsequent evaluation). If there are any issues, an output is provided via the systems and methods described herein indicating that signal acquisition should be performed again (e.g., if the acquired signal is unacceptable, reject the acquired signal and acquire a new one).

Description

Technical Field

[0001] Related Applications This international PCT application claims the priority and benefit of U.S. 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] <s [[ID=z18]]Ischemic heart disease, also known as cardiac ischemia or myocardial 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 any change from the normal electrical conduction sequence of the heart, and in some cases, can lead to cardiac arrest.

[0004] The assessment of cardiac cardiopathy (CAD) can be complex, and many techniques and tools are used to assess the presence and severity of symptoms. In electrocardiography, a branch of cardiology that analyzes the electrical activity of the heart to obtain information about its structure and function, severe ischemic heart disease can alter the ventricular conduction characteristics of the myocardium within the perfusion bed located downstream of the narrowing or occlusion of the coronary arteries. This condition can manifest in various locations and at various stages of severity in the heart, which makes accurate diagnosis difficult. Furthermore, the electrical conduction characteristics of the myocardium can vary from person to person, and other factors such as measurement variability related to the placement of the measurement probe and parasitic losses related to such probes and their associated components can also affect the biophysical signals obtained during electrophysiological examination 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 nonlinear fluctuations that cannot be efficiently captured by conventional modeling techniques.

[0005] Whether cardiac signals, nerve signals, or other biophysical signals, the signal quality of acquired biophysical signals can be affected by noise. Such noise, which can originate from various sources, can affect the patient's assessment, including clinical evaluation of the patient's biological system or the system associated with such signals, as well as any related symptoms or conditions. In the case of cardiac signals, such noise can affect some or all of the acquired signals, reducing the effectiveness of assessments for CAD, arrhythmias, pulmonary hypertension, heart failure, and, for example, any symptoms or conditions related to, concerning, or (directly or indirectly) affecting cardiac signals, thereby increasing the risk of giving patients false assessments and erroneous diagnoses.

[0006] Furthermore, if problems such as degraded signal quality have a negative impact, it may be necessary to ignore some or all of the acquired signals and acquire new signals from the patient. In some cases, this may require re-evaluation, causing inconvenience to the patient who has to return to a clinic, hospital, or other medical setting, and potentially incurring additional costs for the healthcare system. [Overview of the project] [Means for solving the problem]

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

[0008] As used herein, the term “cardiac signals” refers to one or more signals relating to the structure, function, and / or activity of the cardiovascular system, including aspects relating to the electrical / electrochemical conductivity of those signals, including, for example, aspects that cause myocardial contraction. In some embodiments, cardiac signals may include electrocardiogram signals, such as those obtained via an electrocardiogram (ECG) or other modalities.

[0009] As used herein, the term “neurological signal” means one or more signals relating 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, and includes aspects relating to the electrical / electrochemical conduction of such signals. In some embodiments, the neurological signal may include electroencephalogram (EEG) signals, such as those obtained via electroencephalography (EEG) or other modalities.

[0010] As used herein, the term “biophysical signal” encompasses all physiological signals from which information can be obtained, and is not limited to cardiac, neurological, or photoplethysmography signals. While not intended to be an exhaustive example, biophysical signals can be classified by type or category, including, for example, electrical (e.g., specific cardiac and nervous system-related signals that can be observed, identified, and / or quantified by techniques such as the measurement 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 reflectivity, interferometry, spectroscopy, absorbance, transmittance, visual observation, photoplethysmography, and similar methods), acoustic, chemical, mechanical (e.g., signals related to fluid flow, pressure, motion, vibration, displacement, strain), thermal, and electrochemical (signals that can be correlated with the presence of specific analytes such as glucose). Biophysical signals may, in some cases, be described in terms of physiological systems (e.g., respiratory system, circulatory system (cardiovascular, lungs), nervous system, lymphatic system, endocrine system, digestive system, excretory system, muscular system, skeletal system, renal / urinary / excretory system, immune system, cutaneous / exocrine system, reproductive system), organ systems (e.g., signals that may be specific to the heart and lungs when they work together), or in terms 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 stated, the term “acquisition of biophysical signals” generally refers to any passive or active means of acquiring biophysical signals from physiological systems, such as mammalian or non-mammalian organisms. The acquisition of passive biophysical signals generally refers to the observation of natural or induced electrical, magnetic, optical, and / or acoustic emittances in relation to body tissues.Non-limiting examples of means for acquiring passive and active biophysical signals include, for example, other non-active methods for observing voltage / potential, current, magnetic, acoustic, optical, and natural emittances with respect to body tissue, and possibly methods for inducing such emittances. Non-limiting examples of means for acquiring passive and active biophysical signals 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 ionization energy or radiation (e.g., X-rays). Acquisition of active biophysical signals may also include the transmission of ionization energy or radiation (e.g., X-rays) (also referred to as "ionized biophysical signals") to body tissue. Means for acquiring passive and active biophysical signals can be performed via invasive procedures (e.g., via surgery or via invasive radiation-mediated protocols) or non-invasively (e.g., via imaging).

[0011] This disclosure relates to the useful quantification of biophysical signal quality in the diagnosis and treatment of cardiac and / or neurological conditions and symptoms; however, such quantification can be applied to the diagnosis and treatment (including surgical, minimally invasive, and / or pharmacological) of any condition and symptom in which biophysical signals are involved in any relevant system of the body. An example in cardiac conditions is the diagnosis of CAD and the subsequent, either alone or in combination, of any number of treatments, such as stent placement in the coronary arteries, atherectomy, angioplasty, prescription of drug therapy, and / or prescription of exercise, nutrition, or other lifestyle changes. Other cardiac conditions or symptoms that may be diagnosed include, for example, arrhythmias, 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 thrombosis, and pulmonary hypertension due to other diseases or disorders such as blood disorders), and other cardiac conditions, symptoms, and / or diseases. Non-limiting examples of neurological conditions, conditions, or symptoms that may be diagnosed include, for example, epilepsy, schizophrenia, Parkinson's disease, Alzheimer's disease (and all other forms of dementia), autism spectrum disorder (including Asperger's syndrome), attention deficit hyperactivity disorder, Huntington's disease, muscular dystrophy, depression, bipolar disorder, brain / spinal tumors (malignant and benign), motor disorders, cognitive disorders, speech disorders, various psychoses, brain / spinal / nerve injuries, chronic traumatic encephalopathy, cluster headaches, and hemiplegia. This includes headaches, neurological disorders (including various forms such as peripheral neuropathy), phantom limb / phantom limb pain, chronic fatigue syndrome, acute and chronic pain (including lower back pain, spinal surgery failure syndrome, etc.), motor disorders, anxiety disorders, symptoms caused by infection or foreign substances (e.g., Lyme disease, encephalitis, rabies), somnolencephalitis and other sleep disorders, post-traumatic stress disorder, neurological symptoms / effects associated with stroke, aneurysms, hemorrhagic injuries, etc., tinnitus and other auditory disorders / symptoms, and visual disorders / symptoms.

[0012] Skeletal-muscle related signals (e.g., electromyography (EMG)) are often characterized as "in-band noise" with respect to cardiac signals, nerve signals, etc., meaning they frequently occur within the same or similar frequency range as the biophysical signal of interest being acquired. For example, in the case of cardiac signals, the main frequency components of the generated signal are often in the range of approximately 0.5 Hz to 80 Hz. Similarly, in the case of neurological signals such as brain signals, the frequency components are often in the range of approximately 0.1 Hz to 50 Hz. Furthermore, depending on the degree of contamination, skeletal-muscle related signals can also have the same or similar amplitude as typical cardiac-based and nerve-based waveforms, etc. In fact, the similarity of skeletal-muscle related signals to cardiac signals, neurological signals, and other biophysical signals can cause significant problems in the analysis of the biophysical signal of interest. Therefore, quantifying the signal quality of measured biophysical signals can be important, for example, to assess the quality of acquired biophysical signals of interest, to reject the use of contaminated acquired signals in subsequent analyses, and to provide useful information for subsequent analyses, enabling compensation for contamination.

[0013] The methods and systems described in various embodiments herein are not limited thereto and may be used in any context relating to another one or more physiological systems, organs, tissues, cells, etc., of a living organism. Two types of biophysical signals that may be useful in cardiovascular contexts, for illustrative purposes only, include cardiac signals that can be obtained via conventional electrocardiogram (ECG / EKG) equipment, bipolar broadband biopotential (cardiac) signals that can be obtained from other equipment such as those described herein, and signals that can be obtained by various plethysmographic techniques, such as photoplethysmography.

[0014] In the context of this disclosure, techniques for acquiring and analyzing biophysical signals are described in particular with respect to their use in diagnosing the presence, absence, location (where applicable), and / or severity of specific disease conditions or symptoms in, related to, 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 cardiological system. Pulmonary hypertension (PH) generally refers to high blood pressure in the arteries of the lungs and can include a variety of symptoms. PH typically has a complex, multifactorial etiology and presents with an insidious clinical onset, although the severity varies. PH can lead to complications such as right heart failure and is often fatal. The World Health Organization (WHO) classifies PH into five groups or types. The first PH group classified by the WHO is pulmonary arterial hypertension (PAH). PAH is a chronic disease characterized by tightening and hardening of the walls of the pulmonary arteries, and is currently incurable. Diagnosis of PAH requires at least 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 higher on cardiac catheterization. One form of pulmonary arterial hypertension (PAH) is known as idiopathic pulmonary arterial hypertension (PHA), which develops without a clear cause. Subcategories of PAH include hereditary PAH, drug and toxin-induced PAH, and PAH associated with other systemic diseases such as connective tissue disease, HIV infection, portal hypertension, and congenital heart disease. PAH encompasses all causes leading to structural narrowing of the pulmonary arteries. In PAH, progressive narrowing of the pulmonary artery bed is due to an imbalance of vasoactive mediators, including prostacyclin, nitric oxide, and endothelin-1. This results in increased right ventricular afterload, leading to right heart failure and premature death. The second group of PH classified by the WHO is pulmonary hypertension due to left heart disease. Disorders in this group are generally characterized by problems on the left side of the heart. Such problems can lead to changes in the pulmonary arteries over time. Specific subgroups include left ventricular systolic dysfunction, left ventricular diastolic dysfunction, valvular heart disease, and even congenital cardiomyopathy and occlusion not related to valvular heart disease. Treatment for this second group of PH tends to focus on the underlying problem (e.g., surgery to replace heart valves, various drug therapies, etc.). The third group of PH classified by the WHO is large and diverse and generally associated with pulmonary disease or hypoxia.The subgroups include chronic obstructive pulmonary disease, interstitial lung disease, sleep-disordered breathing, alveolar hypoventilation, chronic high-altitude exposure, and developmental lung disease. The fourth PH group, as classified by the WHO, is chronic thromboembolic pulmonary hypertension, caused by obstruction of blood flow through the pulmonary arteries due to the entry or formation of blood clots in the lungs. The fifth PH group, as classified by the WHO, includes rare diseases that cause PH, such as hematological disorders, systemic diseases such as sarcoidosis with pulmonary lesions, metabolic diseases, and other diseases. The mechanism of PH in this fifth 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 so many other medical conditions, diagnosing all forms of PH in a routine examination can be difficult. 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 specific forms of PH. As mentioned above, the "gold standard" for diagnosing PH, especially PAH, is right ventricular catheterization by directly measuring pulmonary artery pressure. If PAH is suspected in a patient, several tests may be performed to confirm the condition, including, in particular, electrocardiograms, chest X-rays, and pulmonary function tests. Typically, evidence of right ventricular tension on the electrocardiogram and pulmonary artery prominence or cardiac hypertrophy on the chest X-ray are seen. However, a normal electrocardiogram and chest X-ray do not necessarily rule out a diagnosis of PAH. Further tests may be necessary to confirm the diagnosis and determine the cause and severity. For example, blood tests, exercise stress tests, and nocturnal oxygen saturation tests may be performed. Imaging tests may also be performed. Examples of imaging tests include isotope perfusion lung scans, high-resolution computed tomography, computed tomography pulmonary angiography, and magnetic resonance pulmonary angiography. If a diagnosis of PAH is made by these (and possibly other) non-invasive tests, right heart catheterization is typically required to confirm the diagnosis by directly measuring pulmonary artery pressure. Measurement of cardiac output and estimation of left atrial pressure using pulmonary artery wedge pressure are also possible. Although non-invasive techniques exist to determine 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 relating to methods and systems for evaluating PH are disclosed in the jointly owned U.S. Patent Application No. 16 / 429,593, which is incorporated herein by reference in its entirety.

[0017] Heart failure affects approximately 6 million people in the United States alone, with over 870,000 new cases diagnosed each year. The term “heart failure” (sometimes referred to as congestive heart failure or CHF) generally refers to a chronic, progressive condition or process in which the heart muscle is unable to pump enough blood to meet the body’s needs due to weakening or hardening of the heart muscle, or due to defects that impede proper circulation. This can result in symptoms such as blood and fluid retention in the lungs, edema, fatigue, dizziness, fainting, rapid and / or irregular heartbeat, dry cough, nausea, and shortness of breath. Common causes of heart failure include coronary artery disease (CAD), hypertension, cardiomyopathy, arrhythmias, kidney disease, heart defects, obesity, smoking, and diabetes. Diastolic heart failure (DHF), left or right-sided heart failure / disease (also known as left ventricular heart failure), right or right-sided heart failure / disease (also known 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, radiography, echocardiography, electrocardiogram (EKG), electrophysiological testing, radionuclide imaging, and various treadmill tests, including tests to measure peak VO2. Ejection fraction (EF) is a measure expressed as the percentage of blood pumped out each time the ventricle contracts (in the case of left-sided heart failure), and is most often obtained non-invasively by echocardiography. The normal left ventricular ejection fraction (LVEF) ranges from approximately 55% to approximately 70%.

[0019] When systolic failure occurs, the left ventricle cannot contract with enough force to keep blood circulating normally throughout the body, resulting in a lack of proper blood supply. When the left ventricle's pumping function is compromised, it weakens and thins. As a result, blood flows back into organs, causing fluid accumulation in the lungs or swelling in other parts of the body. Echocardiography, magnetic resonance imaging, and nuclear medicine scans (multi-gate acquisition, etc.) are techniques used to non-invasively measure ejection fraction (EF), expressed as the percentage of blood pumped out of the left ventricle relative to its filling volume, and are helpful in diagnosing systolic failure. In particular, a left ventricular ejection fraction (LVEF) of less than 55% indicates below-normal cardiac pumping function, and in severe cases, it may be measured as less than approximately 35%. Generally, a diagnosis of systolic failure can be made or aided by these LVEF values ​​being below normal.

[0020] When diastolic heart failure occurs, the left ventricle becomes stiff and thickened, losing its ability to relax properly. This means that the left lower ventricle of the heart is no longer adequately filled with blood. As a result, the amount of blood pumped to the body decreases. If this continues, blood accumulates in the left atrium and then in the lungs, leading to fluid retention and symptoms of heart failure. In this case, the LVEF tends to remain within the normal range. Therefore, measuring left ventricular end-diastolic pressure (LVEDP) using other tests, such as invasive catheterization, may be helpful in diagnosing diastolic heart failure and other forms of heart failure in which EF is preserved. Typically, LVEDP is measured directly by placing a catheter in the left ventricle, or indirectly by measuring pulmonary capillary wedge pressure by placing a catheter in the pulmonary artery. Due to their nature, these catheterization techniques tend to increase the patient's risk of infection and other complications, and are also 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 when diagnosing the numerous other forms of heart failure in which EF is preserved. 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 the numerous other forms of heart failure in which EF is preserved, without necessarily involving the determination or estimation of abnormal LVEDP. Embodiments of this disclosure address all of these needs.

[0021] Right-sided heart failure often occurs due to left-sided heart failure when a weakened and / or stiffened left ventricle loses its ability to efficiently pump blood to the rest of the body. As a result, fluid regurgitates through the lungs, weakening the right side of the heart and causing right-sided heart failure. This regurgitation can also cause venous reflux, leading to fluid edema in the legs, ankles, digestive 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 though the left side of the heart is functioning normally. Procedures and techniques commonly used to determine whether a patient has left-sided heart failure include blood tests, cardiac CT scans, cardiac catheterization, X-rays, coronary angiography, echocardiography, electrocardiogram (EKG), myocardial biopsy, pulmonary function tests, and various forms of stress tests, such as the treadmill test.

[0022] Pulmonary hypertension is closely associated with heart failure. As mentioned above, PAH (the first PH group according to the WHO classification) can lead to increased right ventricular afterload, right-sided heart failure, and premature death. PH due to left-sided heart failure (the second PH group according to the WHO classification) is considered the most common cause of PH.

[0023] Ischemic heart disease, also known as cardiac ischemia or myocardial ischemia, and associated symptoms or conditions may also be estimated or diagnosed using the techniques disclosed herein. Ischemic heart disease is a disease or group of diseases characterized by reduced blood supply to the myocardium, usually caused by coronary artery disease (CAD). CAD is closely related to and is the most common cause of heart failure. CAD typically occurs when the intima of the coronary arteries, which supply blood to the myocardium, develops atherosclerosis (hardening or stiffening of the intima, accumulation of plaque, and often abnormal inflammation). Over time, CAD weakens the myocardium and can lead to conditions such as angina pectoris, myocardial infarction (cardiac arrest), heart failure, and arrhythmias. An arrhythmia is an abnormal heart rhythm that may involve some deviation from the normal electrical conduction sequence of the heart, and in some cases may lead to cardiac arrest. The assessment of PH, heart failure, CAD, and other diseases and / or conditions can be complex, and as mentioned above, many invasive techniques and tools are used to assess the presence and severity of symptoms. In addition, the commonalities in the symptoms of these diseases and / or conditions, and the fundamental coordination between the respiratory and cardiovascular systems, which work together to supply oxygen to the body's cells and tissues, indicate complex physiological interrelationships that could be utilized to improve the detection and final treatment of such diseases and / or conditions. In this context, conventional methodologies for evaluating these biophysical signals still present significant challenges in providing healthcare professionals with tools to accurately detect / diagnose the presence or absence of such diseases and conditions.

[0024] For example, in electrocardiography (ECG), a branch of cardiology that analyzes the electrical activity of the heart to obtain information about its structure and function, it has been observed that severe ischemic heart disease alters the ventricular conduction characteristics of the myocardium within the perfusion bed located downstream of the narrowing or occlusion of the coronary arteries. The condition can manifest in 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 variability related to the placement of the measurement probe, and parasitic losses related to such probes and their associated components, can also affect the biophysical signals obtained during electrophysiological examinations 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 nonlinear fluctuations that cannot be efficiently captured by conventional modeling techniques.

[0025] In one embodiment, a method is disclosed for acquiring a biophysical signal dataset for clinical analysis (e.g., as part of a machine learning dataset or for clinical diagnosis), wherein a processor acquires a biophysical signal dataset or a portion thereof relating to a subject (e.g., relating to the subject's heart, brain, lungs, etc.) for measurement, acquiring the biophysical signal dataset or a portion thereof via one or more surface probes (e.g., placed on the subject's chest) of a non-invasive measurement system on one or more corresponding channels, and acquiring for an acquisition duration suitable for subsequent evaluation (e.g., more than approximately 120 seconds, e.g., approximately 210 seconds), wherein the acquisition duration is predetermined, dynamically determined, or set by the user; and determining one or more signal quality parameters with respect to the acquired biophysical signal dataset by a processor (e.g., the processor of the non-invasive measurement system), wherein at least one of the one or more signal quality parameters is defined as power line noise interference. The parameters shall be selected from the group consisting of power line interference parameters related to the above, high-frequency noise parameters related to high-frequency noise contamination, noise burst parameters related to high-frequency noise burst contamination, sudden movement parameters related to sudden movement contamination, and asynchronous noise parameters related to skeletal muscle contamination or cardiac cycle variability, and if one or more signal quality parameters fail the noise quality assessment performed on one or more signal quality parameters, the processor shall reject the acquired biophysical signal dataset or the assessment portion thereof (for example, rejection causes the processor to output a visual indicator, an audible indicator, or a report regarding the assessment that failed in the non-invasive measurement system, and this output is performed simultaneously with or nearly simultaneously with the measurement) (for example, rejection prompts the acquisition of a second biophysical signal dataset or a portion thereof for the subject immediately after the acquisition of the biophysical signal) (for example,The evaluation of non-rejection, i.e., acceptance, of the acquired biophysical signal dataset includes causing the acquired biophysical signal dataset to be transmitted to a processor via 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 an evaluation that was不合格 in a non-invasive measurement system, and the output is performed simultaneously with the measurement or substantially simultaneously (e.g., immediately after acquiring a biophysical signal, to prompt acquisition of a second biophysical signal dataset or a portion thereof regarding the subject).

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

[0028] In some embodiments, the method includes acquiring a voltage gradient signal on one or more channels by one or more acquisition circuits of the measurement system, acquiring the voltage gradient signal at a frequency greater than about 1 kHz, and generating a biophysical data set acquired from the acquired voltage gradient signal by one or more acquisition circuits. In some embodiments, the method further includes 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, and arranging at least a third surface probe at a third axis along which the body of the subject passes from front to back, wherein the first axis, the second axis, and the third axis are arranged to be mutually orthogonal axes.

[0029] In some embodiments, the acquired biophysical signal dataset or evaluation portion is rejected when the power line interference parameters for one or more channels fail to meet the power line interference conditions (for example, when they exceed the power line interference threshold).

[0030] In some embodiments, the acquired biophysical signal dataset or evaluation portion is rejected when, with respect to one or more channels, the high-frequency noise parameters associated with high-frequency noise contamination fail to meet the high-frequency noise conditions (for example, when the high-frequency noise score exceeds a predetermined high-frequency noise threshold).

[0031] In some embodiments, the acquired biophysical signal dataset or evaluation portion is rejected when the noise burst parameter related to high-frequency noise burst contamination with respect to one or more channels fails to meet the noise conditions (for example, a 1-second window is tested using a high-frequency time series, and the 1-second window is compared to the median of the high-frequency energy; the biophysical signal dataset is rejected when the 1-second energy is greater than twice the median).

[0032] In some embodiments, the acquired biophysical signal dataset or evaluation portion is rejected when the transient motion parameter associated with transient motion contamination with respect to one or more channels fails to meet the transient motion conditions (for example, when the baseline within a 1-second window of the signal changes by more than 25% of the channel's ventricular depolarization amplitude relative to the previous window).

[0033] In some embodiments, the acquired biophysical signal dataset or evaluation portion is rejected when an asynchronous noise parameter, which may include skeletal muscle contamination or cardiac cycle variability, fails to meet the asynchronous noise conditions (for example, when periodic variability noise exceeds a predetermined threshold) with respect to one or more channels.

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

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

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

[0037] In some embodiments, the biophysical signal dataset is generated in near real-time with respect to the acquisition of biophysical signals.

[0038] In some embodiments, biophysical signals are acquired from sensors within a smart device or 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 multiple surface electrodes placed on a body surface adjacent to the subject's heart.

[0040] In another embodiment, a method for rejecting acquired biophysical signals is disclosed, the method comprising: a processor receiving a biophysical signal dataset of a subject; the processor comparing 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 fluctuations; and, based on the comparison, the processor rejecting the received biophysical signal dataset.

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

[0042] In some embodiments, comparing a received biophysical signal dataset with high-frequency noise includes determining a high-frequency noise score for the biophysical signal dataset, and rejecting a 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 a received biophysical signal dataset to a high-frequency noise burst includes determining the high-frequency noise burst of the biophysical signal dataset using a high-frequency time series to test a 1-second window and comparing the 1-second window to a threshold, and rejecting a received biophysical signal dataset includes rejecting the biophysical signal dataset when the 1-second energy is greater than a threshold.

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

[0045] In some embodiments, comparing a received biophysical signal dataset with periodic fluctuations includes determining periodic fluctuation noise, and rejecting a received biophysical signal dataset includes rejecting the biophysical signal dataset when the periodic fluctuation noise exceeds a predetermined threshold.

[0046] In some embodiments, the comparison includes determining the presence of asynchronous noise present in the acquired biophysical signal dataset that has a value or energy exceeding a predetermined threshold.

[0047] In some embodiments, the method further includes the processor generating a notification that the acquisition of the biophysical signal dataset failed. In some embodiments, the notification prompts the system to subsequently perform the acquisition of the biophysical signal dataset.

[0048] In some embodiments, the method further includes causing a processor to transmit the received biophysical signal dataset over a network to an external analysis system, which is configured to analyze the received biophysical signal data for the presence or degree of a disease or clinical symptom.

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

[0050] In another embodiment, a non-temporary computer-readable medium having stored instructions is disclosed, which, by executing instructions by one or more processors, causes 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 biophysical signal datasets for clinical analysis, The processor acquires a biophysical signal dataset or a portion thereof relating to a subject for measurement, by acquiring the biophysical signal dataset or a portion thereof via one or more surface probes of a non-invasive measurement system on one or more corresponding channels, and by acquiring it over an acquisition duration suitable for subsequent evaluation, wherein the acquisition duration is predetermined, dynamically determined, or set by the user. The processor and / or remotely by one or more cloud-based services or systems determines one or more signal quality parameters with respect to the acquired biophysical signal dataset, wherein at least one of the one or more signal quality parameters is selected from the group consisting of power line interference parameters related to power line noise contamination, high-frequency noise parameters related to high-frequency noise contamination, noise burst parameters related to high-frequency noise burst contamination, sudden movement parameters related to sudden movement contamination, and asynchronous noise parameters related to skeletal muscle contamination or cardiac cycle variability. A method comprising: if one or more of the signal quality parameters fail a noise quality evaluation performed on the one or more of the signal quality parameters, the processor rejects the acquired biophysical signal dataset or the evaluation portion thereof. (Item 2) The method according to item 1, further comprising outputting one or more of a visual indicator, an audible indicator, a vibration indicator, and a report regarding an evaluation that failed in the non-invasive measurement system, wherein the output is performed simultaneously with or nearly simultaneously with the measurement. (Item 3) The method of item 1 or 2, further comprising transmitting the acquired biophysical signal dataset via a network by the processor for remote clinical analysis after evaluation of non-rejection, i.e., after evaluation of acceptance, with respect to the acquired biophysical signal dataset. (Item 4) The measurement system acquires a voltage gradient signal on one or more channels using one or more acquisition circuits, wherein the voltage gradient signal is acquired at a frequency greater than approximately 1 kHz. The method according to any one of items 1 to 3, further comprising generating the acquired biophysical dataset from the acquired voltage gradient signal using the one or more acquisition circuits. (Item 5) At least the first surface probe should be positioned along the first axis that passes through the subject's body from left to right, At least the second surface probe is positioned along a second axis that passes through the subject's body from top to bottom, The method according to any one of items 1 to 4, further comprising positioning at least a third surface probe on a third axis that passes through the body of the subject from front to back, wherein the first axis, the second axis, and the third axis are mutually orthogonal axes. (Item 6) If the power line interference parameter for any one of the aforementioned channels fails to meet the power line interference conditions, the acquired biophysical signal dataset or the The method described in any one of items 1-5 to reject the evaluation portion. (Item 7) The method according to any one of items 1 to 6, wherein if the high-frequency noise parameter related to high-frequency noise contamination with respect to any of the one or more channels fails to meet the high-frequency noise conditions, the acquired biophysical signal dataset or the evaluation portion is rejected. (Item 8) The method according to any one of items 1 to 7, wherein if the noise burst parameter related to high-frequency noise burst contamination with respect to any of the one or more channels fails to meet the noise conditions, the acquired biophysical signal dataset or the evaluation portion is rejected. (Item 9) The method according to any one of items 1 to 8, wherein if the sudden movement parameter associated with sudden movement contamination with respect to any one of the aforementioned one or more channels fails to meet the sudden movement conditions, the acquired biophysical signal dataset or the evaluation portion is rejected. (Item 10) The method according to any one of items 1 to 9, wherein if an asynchronous noise parameter that may include skeletal muscle contamination or cardiac cycle variation with respect to any of the one or more channels fails to meet the asynchronous noise conditions, the acquired biophysical signal dataset or the evaluation portion is rejected. (Item 11) The power line coefficients related to the aforementioned power line interference parameters are, The processor performs a Fourier transform on the acquired biophysical signal dataset or a portion thereof. The method according to any one of items 1 to 10, wherein the processor determines the maximum value of the power line energy in multiple frequency ranges. (Item 12) The method according to any one of items 1 to 11, wherein the aforementioned evaluation serves as 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 biophysical signal dataset to be received 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 in near real-time with respect to the acquisition of biophysical signals. (Item 15) The method according to any one of items 1 to 14, wherein the biophysical signal is obtained from a sensor in a smart device or 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 acquired simultaneously from a plurality of surface electrodes placed on a body surface adjacent to the heart of the subject. (Item 17) A method for rejecting acquired biophysical signals, The processor receives the biophysical signal dataset of the subject, The processor and / or remotely by one or more cloud-based services or systems compare 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 fluctuations. A method comprising rejecting the received biophysical signal dataset by the processor based on the comparison described above. (Item 18) The method according to item 17, wherein comparing the received biophysical signal dataset with the power line interference includes determining the 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. (Item 19) The method according to item 16 or 17, wherein comparing the received biophysical signal dataset with the high-frequency noise includes determining the 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. (Item 20) The method according to any one of items 16 to 19, wherein comparing the received biophysical signal dataset with the high-frequency noise burst comprises determining the high-frequency noise burst of the biophysical signal dataset 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 dataset comprises rejecting the biophysical signal dataset when the 1-second energy is greater than a threshold. (Item 21) The method according to any one of items 16 to 20, wherein comparing the received biophysical signal dataset with the sudden baseline shift includes determining a sudden baseline shift when the baseline of the signal within a predetermined time window has changed by more than a predetermined amount relative to the previous window, and rejecting the received biophysical signal dataset includes rejecting the biophysical signal dataset when the sudden baseline shift has been determined. (Item 22) The method according to any one of items 16 to 21, wherein comparing the received biophysical signal dataset with the periodic fluctuations includes determining the periodic fluctuation noise, and rejecting the received biophysical signal dataset includes rejecting the biophysical signal dataset when the periodic fluctuation noise exceeds a predetermined threshold. (Item 23) The method according to any one of items 16 to 22, wherein the comparison includes determining the presence of asynchronous noise present in the acquired biophysical signal dataset having a value or energy exceeding a predetermined threshold. (Item 24) The method according to any one of items 1 to 23, further comprising the processor generating a notification that the acquisition of the biophysical signal dataset was unsuccessful. (Item 25) The method described in any one of items 1 to 24, which prompts the notification to subsequently perform the acquisition of the biophysical signal dataset. (Item 26) The method according to any one of items 1 to 25, further comprising causing the processor to transmit the received biophysical signal dataset to an external analysis system via a network, and configuring the analysis system to analyze the received biophysical signal data for the presence or degree of a disease or clinical symptom. (Item 27) It is a system, One or more processors, A system comprising: a memory having instructions stored on one or more processors, wherein the execution of the instructions by the one or more processors causes the one or more processors to perform the method described in any one of items 1 to 26. (Item 28) A non-temporary computer-readable medium having stored instructions, wherein the execution of the instructions by one or more processors causes the one or more processors to perform the method described in any one of items 1 to 26.

[0051] The accompanying drawings, incorporated herein and constituting part of this specification, illustrate embodiments and, together with this specification, serve to illustrate the principles relating to the methods and systems contained herein. Embodiments can be better understood by reading them together with the accompanying drawings, based on the following detailed description. The drawings include the following figures. [Brief explanation of the drawing]

[0052] [Figure 1A] Figure 1A shows an exemplary system configured to quantify and remove asynchronous noise and artifacts in order to more accurately evaluate complex nonlinear fluctuations in quasi-periodic lines, such as biological lines with biophysical signals, according to an exemplary embodiment. [Figure 1B] Figure 1B shows an exemplary system configured to reject acquired biophysical signals based on the quantification of asynchronous noise and artifact contamination, according to another exemplary embodiment. [Figure 2] Figure 2 shows an exemplary evaluation system according to an exemplary embodiment. [Figure 3] Figure 3 shows an exemplary signal quality evaluation system according to an exemplary embodiment. [Figure 4] Figure 4 is an operational flowchart illustrating the implementation of a method for evaluating signal quality according to another exemplary embodiment. [Figure 5] Figure 5 is an operational flowchart illustrating the implementation of a method for evaluating power line interference according to another exemplary embodiment. [Figure 6]Figure 6 shows an observable characteristic, namely maximum power line interference, in the power line interference evaluation operation of Figure 5, for example, according to another exemplary embodiment. [Figure 7] Figure 7 shows an observable characteristic, power line interference at a threshold, in the power line interference evaluation operation of Figure 5, for example, according to an exemplary embodiment. [Figure 8] Figure 8 is an operational flowchart illustrating the implementation of a method for evaluating high-frequency noise according to an exemplary embodiment. [Figure 9] Figure 9 shows an observable characteristic, the maximum high-frequency noise, in the high-frequency noise evaluation operation, for example, in Figure 8, according to an exemplary embodiment. [Figure 10] Figure 10 shows an observable characteristic, the high-frequency noise at the threshold, in the high-frequency noise evaluation operation, for example, in Figure 8, according to an exemplary embodiment. [Figure 11] Figure 11 is an operational flowchart illustrating the implementation of a method for evaluating high-frequency noise bursts according to an exemplary embodiment. [Figure 12] Figure 12 shows an observable characteristic, the maximum high-frequency noise burst, in the high-frequency noise burst evaluation operation of, for example, Figure 11, according to an exemplary embodiment. [Figure 13] Figure 13 shows an observable characteristic, the second maximum high-frequency noise burst, in the high-frequency noise burst evaluation operation of, for example, Figure 11, according to an exemplary embodiment. [Figure 14] Figure 14 is an operational flowchart illustrating the implementation of a method for evaluating sudden baseline shifts according to an exemplary embodiment. [Figure 15] Figure 15 shows an observable characteristic, the maximum sudden movement, in an exemplary embodiment, for example, in the sudden baseline movement evaluation operation shown in Figure 14. [Figure 16] Figure 16 shows an observable characteristic of 50% sudden movement in the sudden baseline movement evaluation operation, for example, in Figure 14, according to an exemplary embodiment. [Figure 17]Figure 17 is an operational flowchart illustrating the implementation of a method for evaluating periodic fluctuations according to an exemplary embodiment. [Figure 18] Figure 18 shows an observable characteristic, the maximum periodic fluctuation noise, in the periodic fluctuation evaluation operation shown in Figure 17, according to an exemplary embodiment. [Figure 19] Figure 19 shows an observable characteristic, for example, smaller periodic noise, in the periodic fluctuation evaluation operation of Figure 17, according to an exemplary embodiment. [Figure 20] Figure 20 is an operational flowchart illustrating the implementation of a method for evaluating signal quality according to an exemplary embodiment. [Figure 21A] Figures 21A and 21B show the architecture and data flow of an exemplary signal quality evaluation component according to an exemplary embodiment. [Figure 21B] Same as above. [Figure 22] Figure 22 shows an exemplary computing environment that can implement exemplary embodiments and aspects according to an exemplary embodiment. [Modes for carrying out the invention]

[0053] Each feature point and all feature points described herein, as well as each and every combination of two or more such feature points, are included within the scope of the present invention, provided that the feature points contained within such combinations do not contradict each other.

[0054] As further described herein, 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 technician in charge. 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) verification of the minimum signal-to-noise ratio (SNR), and / or (5) detection of asynchronous noise (e.g., electromyography (EMG) noise).

[0055] As will be further described herein, a signal is acquired, a decision is made in real time as to whether there are any problems with the acquisition (for example, the acquired signal is processed immediately to determine whether the acquired signal is acceptable or unacceptable, or 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] Figure 1A shows an exemplary system 100 configured, according to an exemplary embodiment, to quantify and remove asynchronous noise, such as skeletal muscle-related artifact noise, and to use such quantification to more accurately assess complex nonlinear fluctuations in a quasi-periodic system. As used herein, the term “remove,” and other similar terms, means to significantly reduce noise contamination, either overall or partially, in order to improve or be beneficial to subsequent analysis.

[0057] In Figure 1A, the measurement system 102 is a non-invasive embodiment (illustrated as “Measurement System (Biophysical)” 102) that acquires multiple biophysical signals 104 from a subject 106 via an arbitrary number of measurement probes 114 (illustrated as including six such probes 114a, 114b, 114c, 114d, 114e, and 114f in system 100 of Figure 1) and generates a biophysical signal dataset 108 available in a non-invasive biophysical signal evaluation system 110 to determine a clinical output 112. In some embodiments, the clinical output includes an assessment of the presence or absence of disease and / or estimated physiological characteristics with respect to the physiological lineage under study. In other embodiments, there is no clinical output; rather, the signals are output as information that a clinician can use to independently clinically evaluate information relevant to the patient being evaluated.

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

[0059] In some embodiments, the measurement system 102 is configured to acquire biophysical signals that may be based on the biopotential of the body as biopotential biophysical signals via a biopotential sensing circuit. In cardiac and / or electrocardiogram situations, the measurement system 102 is configured to acquire cardiac-related biopotential or electrophysiological signals related to a living subject (e.g., a human) as a biopotential cardiac signal dataset. In some embodiments, the measurement system 102 is configured to acquire a broadband cardiac phase gradient signal as a biopotential signal or other signal type (e.g., current signal, impedance signal, magnetic signal, optical signal, ultrasound or acoustic signal, etc.). The term “broadband” in relation to the acquired signal and the corresponding dataset refers to a signal having a substantially larger frequency range than the Nyquist sampling rate of the most dominant frequency of the physiological system of interest. Typically, for a cardiac signal with a dominant frequency component between approximately 0.5 Hz and 80 Hz, a broadband cardiac phase gradient signal or broadband cardiac biophysical signal includes cardiac frequency information at frequencies selected from the group consisting of approximately 0.1 Hz to 1 kHz, 0.1 Hz to 2 kHz, 0.1 Hz to 3 kHz, 0.1 Hz to 4 kHz, 0.1 Hz to 5 kHz, 0.1 Hz to 6 kHz, 0.1 Hz to 7 kHz, 0.1 Hz to 8 kHz, 0.1 Hz to 9 kHz, 0.1 Hz to 10 kHz, and above 0.1 Hz to 10 kHz (e.g., 0.1 Hz to 50 kHz, or 0.1 Hz to 500 kHz). In addition to acquiring the dominant frequency component, broadband acquisition also facilitates the acquisition of other frequencies of interest. Examples of such frequencies of interest include, among other things, QRS frequency profiles (which may have a frequency range up to 250 Hz). The term “phase gradient” in relation to acquired signals and corresponding datasets refers to signals acquired from different viewpoints of the body to observe phase information about a series of different events / functions of the physiological system of interest.After signal acquisition, the term "phase gradient" refers to the preservation of phase information using hardware, software, and techniques (e.g., phase-linear filters, signal processing operators, and / or signal processing algorithms) for distortion-free signal processing and preprocessing.

[0060] In neurological contexts, the measurement system 102 is configured to acquire neurological-related biopotential or electrophysiological signals from a living subject (e.g., a human) as a neurological biophysical signal dataset. In some embodiments, the measurement system 102 is configured to acquire broadband neurological phase gradient signals as biopotential signals or other signal types (e.g., current signals, impedance signals, magnetic signals, ultrasound, optical signals, ultrasonic or acoustic signals, 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 incorporated herein by reference in whole.

[0061] In some embodiments, the measurement system 102 is configured to acquire a broadband biopotential biophysical phase gradient signal as an unfiltered electrophysiological signal so 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, transformed, and even analyzed without filtering (e.g., before digitization) (e.g., via hardware circuits 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 located at or significantly below the noise floor of acquisition equipment for conventional electrocardiograms, electroencephalograms, and other biophysical signals. In some embodiments, the broadband biopotential biophysical signal is sampled simultaneously with a temporal skew or "lag" of less than about 1 microsecond, and in other embodiments, with a temporal skew or lag of no more than about 10 femtoseconds. Notably, the illustrated system aims to minimize nonlinear distortions (e.g., distortions that may be introduced through a specific filter) within the acquired broadband phase gradient signal so as not to affect the information contained therein.

[0062] Referring again to Figure 1A, the evaluation system 110 is configured to receive the acquired biophysical signal dataset 108 (in this embodiment, the denoised dataset 108) via, for example, a network, and in some embodiments, is configured to generate one or more three-dimensional vector electrocardiogram datasets 122 by a transformation operation 120 (denoted as “phase space transformation” 120), and further analyze the phase gradient biophysical signal dataset 108 via, for example, one or more machine learning analysis operations and / or one or more prediction operations (illustrated as step 124). Examples of the transformation operation and machine learning / predictor operations are described below and are also described in their entirety in U.S. Patent Application Publication No. 2013 / 0096394, which is incorporated herein by reference. In some embodiments, the acquired biophysical signal dataset 108 is structured as a multidimensional dataset for subsequent processing without explicit transformation, for example, when intermediate datasets are not visualized.

[0063] In some embodiments, the measurement system 102 is configured to evaluate the signal quality of the acquired biophysical signal and, based on such evaluation, reject some or all of the acquired signal dataset. Figure 1B shows an exemplary system configured to reject 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 denoising operation 118 and a signal quality evaluation operation 130 based on the quantification of asynchronous noise.

[0064] Because the clinical analysis of the acquired biophysical signals 108 may, in some embodiments, be performed on a system separate from the measurement system 102 (e.g., an 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, thereby ensuring that the acquired biophysical signal dataset is free from asynchronous noise (e.g., skeletal muscle-related noise) before it undergoes further processing and analysis for clinical evaluation, or before it becomes available for further processing and analysis for clinical evaluation.

[0065] In some embodiments, the signal quality evaluation operation 130 is performed in near real time, for example, in less than 1 minute or less than 5 minutes, in which case the system 102 may prompt the reacquisition of the biophysical signal dataset. This near real-time evaluation allows for the reacquisition of the biophysical signal dataset, if necessary, before the patient leaves the laboratory or before the patient leaves any other location where the biophysical signals are acquired. The analysis performed by the evaluation system 110 to determine the clinical output is performed in some embodiments in approximately 10 to 15 minutes. In other embodiments, this analysis is performed in less than 5 minutes. In yet another embodiment, this analysis is performed in approximately 5 to 10 minutes. In yet another embodiment, this analysis is performed in more than 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 different physical location from the patient (e.g., on one or more computing 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 within a networked environment, including multiple physical locations and multiple computing and / or storage devices. Such a networked environment can ensure the privacy of the patient whose signal is being evaluated, for example, to comply with various privacy requirements.

[0067] In some embodiments, signal evaluation is performed while the signal is being acquired from the patient, for example, at a speed as fast as or nearly fast as the signal evaluation system is operational (e.g., in real time or near real time, depending on the configuration of the signal evaluation system, network constraints, etc.). In other embodiments, signal evaluation is performed partially when the signal is acquired and partially after it has been acquired from the patient and stored. In yet another embodiment, no signals are evaluated when they are acquired from the patient, but instead are stored for evaluation at a later time compared to when they were acquired from the patient. Naturally, all signals may be stored after acquisition for later evaluation or re-evaluation, regardless of when they may be evaluated.

[0068] One or more clinicians may perform a clinical assessment of a patient, either entirely or partially, based on the signal assessment of the patient performed by the systems and methods described herein. Such clinicians may be physically present with the patient and / or physically separated from the patient. The signal assessment systems described herein may also perform a clinical assessment of a patient, either entirely or partially, for example, by the clinical output of one or more operations performed by the signal assessment system. Alternatively, the signal assessment system may simply provide information that does not constitute a clinical assessment for use by the clinician when performing their own clinical assessment of the patient. If the signal assessment system provides a clinical output, the clinician may choose to accept or reject such clinical output when performing their own final clinical assessment of the patient, for example, where such clinician involvement and final decision-making are required or mandated (e.g., by law, protocol, insurance requirements, etc.).

[0069] In some embodiments, the non-invasive measurement system 102 is configured to generate a notification 126 (labeled in Figure 1B as “Signal Quality Assessment Failed” 126) indicating that the acquisition of a biophysical signal dataset was unsuccessful or inadequate, and this notification may also prompt a reacquisition of the biophysical signal. The notification may take any form, such as a visual output (e.g., one or more indicator lights or indicators on a screen), an audio output, a tactile / vibrational output (or any combination thereof), provided to a technician or clinician and / or to the patient. Examples of user interfaces (e.g., graphical user interfaces) of the measurement system 102 that can present, for example, notification 126, are described in U.S. Patent Application Publication No. 2017 / 0119272, entitled “Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition,” filed August 26, 2016, and U.S. Design Application No. 29 / 578,421, entitled “Display with Graphical User Interface,” each of which is incorporated herein by reference in whole. For this purpose, all or part of the rejected biophysical signal datasets may not be used in subsequent analyses (e.g., 120, 124) to obtain clinical output 112.

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

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

[0072] In other embodiments, a clinician or other operator may, alone, in conjunction with, or with assistance from system 200, control whether or not all or part of the rejected biophysical signal datasets can be used, and how they are used or not used.

[0073] Figure 2 shows 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 is initiated by receiving a signal and ends by returning a set consisting of a final evaluation of CAD, the localization of that CAD to one or more affected arteries, and / or one or more phase-spatial tomography datasets / images (also referred to as “PST datasets / 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 thereof. In addition, the PST datasets / images may be output, for example, a two-dimensional or three-dimensional graphic representation of the evaluation, generated via phase-spatial analysis.

[0074] Useful concepts and examples of analysis of phase space 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 26 December 2018, and U.S. Patent Application Publication No. 2019 / 0200893, entitled "Method and System to Assess Disease Using Phase Space Tomography and Machine Learning," each of which is incorporated herein 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 stored phase-spatial datasets / images and / or clinical outputs 112, such as assessments of the presence and / or absence of disease and / or estimated physiological characteristics (among other intermediate datasets) with respect to the physiological lineage under study in phase-spatial analysis and / or angiography-equivalent reports. In some embodiments, the healthcare provider portal may be referred to as a physician portal or clinician portal and is configured to access, retrieve, and / or display or present reports and / or phase-spatial volume datasets / images and / or clinical outputs 112 (and other data) for reports from a repository (e.g., a storage area network).

[0076] In some embodiments, the healthcare provider portal is configured to display phase-space volume datasets / images (or intermediate datasets derived therefrom) and / or clinical output 112 in or alongside anatomical mapping reports, coronary artery tree reports, and / or 17-segment reports. The healthcare provider portal may present the data, for example, in real time (e.g., as web objects), as electronic documents, and / or in other standardized or non-standardized dataset visualization / image visualization / medical data visualization / scientific data visualization formats. In some embodiments, the healthcare provider portal is configured to access and retrieve reports and / or clinical output (and other data) for phase-space volume datasets / images and / or reports from a repository (e.g., a storage area network). The healthcare provider portal and / or repository may comply with laws and regulations relating to the privacy of patient information and other personal data (e.g., the U.S. Healthcare Portability and Accountability Act of 1996 and the EU General Data Protection Regulation, etc.), as well as laws relating to the sale of medical devices (e.g., the U.S. Food and Drug Act and the EU Medical Devices Regulation, etc.). Further description of an exemplary healthcare provider portal is provided in U.S. Patent Application Publication No. 2018 / 0078146, entitled "Method and System for Visualization of Heart Tissue at Risk," which is incorporated herein by reference in its entirety. In certain embodiments, the healthcare provider portal is configured to present patient medical information to healthcare professionals, but in other embodiments, the healthcare provider portal may be 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 region of the affected myocardium. In some embodiments, the anatomical mapping report is configured to display and switch between one or more sets of three-dimensional views and / or sets 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 corresponding regions of the myocardium. In each report, values ​​indicating the presence of cardiac disease or symptoms at a location within the myocardium, as well as labels indicating the presence of cardiac disease, may be rendered as both static and dynamic visualization elements indicating the predicted occlusion region, for example, using color highlighting for the affected myocardium region and animation sequences highlighting the affected one or more coronary artery regions. In some embodiments, each report includes a text label indicating the presence or absence of heart disease (e.g., the presence of severe coronary artery disease) and a text label indicating the presence (i.e., location) of 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 the myocardium at risk and / or occluded coronary arteries. The user interface may be a graphical user interface ("GUI") with a touch-sensitive or pre-touch-sensitive screen with input capabilities. The user interface can be used, for example, to direct the diagnosis and treatment of a patient and / or to evaluate a patient who is the subject of a study. The visualization may include multiple depictions of a rotatable three-dimensional anatomical map of the cardiac region 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, with respect to a given report on the study, thereby facilitating the interpretation and evaluation of structural feature points of the myocardium to characterize abnormalities in cardiac and cardiovascular function.

[0079] In the embodiment shown in Figure 2, the execution of the evaluation system 200 is gated at its inception by the requirement that the signal is of sufficient quality for subsequent evaluation, as will be further described 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 it may not be present at all.

[0080] In system 200, a phase signal recorder (PSR) signal 205 may be received as input, along with data indicating the sex and / or age 210 of the subject (e.g., patient). In one embodiment, the PSR signal 205 (and its corresponding dataset) is an unmodified signal, which 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 the signal 205) is 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 Figures 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 positioned, for example, along three orthogonal axes relating to the subject's body. In one implementation, data from channel "ORTH1" corresponds to a series of bipolar acquired channel data recorded by a phase-space recorder from electrodes 114 positioned along or near one of these orthogonal axes that pass through the subject's body from left to right. Data from channel "ORTH2" corresponds to a second series of bipolar acquired channel data recorded by a phase-space recorder from two other electrodes 114 positioned along or near a second axis of these orthogonal axes that pass through the subject's body from top to bottom. Data from channel "ORTH3" corresponds to a third series of bipolar acquired channel data recorded by a phase-space recorder from two further electrodes 114 positioned along or near a third axis of these orthogonal axes that pass through the subject's body from front to back. The signals from ORTH1, ORTH2, and ORTH3, and their corresponding datasets, can be arranged, for example, on mutually orthogonal axes within a phase space coordinate system.It should be noted that the ORTH1, ORTH2, and ORTH3 signals (e.g., broadband phase gradient biophysical signals) are referred to in some embodiments to more clearly distinguish them from vector electrocardiogram devices.

[0081] As will be further described in some embodiments relating to Figures 3 to 21, a signal quality evaluation system module 300, which may be provided within the measurement system 102 of Figure 1 to perform, for example, operation 130, evaluates the input signal 383 (e.g., PSR signal 205, etc.) to determine whether to proceed with subsequent processing, 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 embodiments, the output of the signal quality evaluation system module 300 is an evaluation of whether to proceed with further analysis (e.g., performed by intermediate processing components 220-240, as understood from Figure 2), and a determination of a time window for a 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 unsuitable (i.e., unacceptable) and that another PSR signal must be acquired in order to proceed with subsequent processing. As described above, in other embodiments, the unacceptable signal quality indication rendered by module 300 may be overridden by system 200 and / or the user, and some or all of the input signal 383 may be used in subsequent processing. In addition, whether or not all or part of the input signal 383 is used in connection with the signal quality evaluation system module 300, such signal 383 may optionally be stored in memory, whole or in part, for analysis and / or future use.

[0082] The intermediate processing components in the embodiment shown in Figure 2 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 (Periodic Variation-Mediated Gender Dependency) compensation module 240. Any or all of these intermediate processing components or modules may or may not be present in other configurations of the system 200, as assumed in other embodiments.

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

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

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

[0086] In the embodiment shown in Figure 2, the prediction combination module 235 receives an overall CAD evaluation from the CNN prediction generation module 220, an overall CAD evaluation from the visual prediction generation module 225, and an overall CAD evaluation from the metadata prediction generation module 230 as input.

[0087] The output from the predictive combination module 235 includes an intermediate, continuous (non-binary), and overall CAD evaluation (referred to as the "intermediate CAD evaluation" in the illustrative formulas below). Further details on how the predictive combination module 235 may operate are provided below.

[0088] Z-score normalization is a statistical technique that centers a distribution at zero and expands or contracts it so that its standard deviation is 1. The output from this process is called the "z-score". As used in some embodiments, z-score normalization is used to ensure that ratings (e.g., ratings from 220, 225, 230) are not only combined by an averaging operation, but also that when all ratings are combined, one or more ratings do not influence one or more other ratings in an undesirable manner (e.g., dominating), i.e., that each rating is equally weighted and / or distributed (e.g., each having the same mean and standard deviation). In other embodiments, z-score normalization may be modified, used in different ways, used in conjunction with additional processing, not used at all, or may be replaced in whole by one or more other techniques to give, as desired, preferential weighting and / or distribution of one or more ratings, etc.

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

number

[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, the average of these outputs is used for women (Equation 3), but only the output of the metadata prediction generation module 230 is used for men (Equation 4). Subsequently, the "visual feature point evaluation" values ​​for women and men are combined, for example, using a union operator.

number

number

[0091] Continuing this exemplary implementation of the embodiment, the output from the CNN evaluation (i.e., the z-score by Equation 2) and the output from the visual feature point evaluation (i.e., the z-score by Equation 6) are then averaged to create an “intermediate CAD evaluation” value as the final output from the predictive combination module 235, as shown in Equation 7. In other words, module 235 sums these z-score outputs for each patient and then divides that sum by 2.

number

[0092] Continuing this implementation of the embodiment, the CVMGD compensation module 240 receives both the subject's sex and / or age data 210 and the intermediate CAD evaluation output from the predictive combination module 235 as input, and outputs a final and continuous (non-binary) CAD evaluation value which may be referred to as the “final and continuous CAD evaluation”. Component 240 utilizes the concept of periodic variability score to calculate a maximum variability score for ORTH1 and ORTH3 (and, in some embodiments, for ORTH2). This periodic variability score acquires information about changes between cardiac cycles and acquires electrophysiological variability that, within a given range of the score, embeds information about CAD symptoms that are observed to be particularly applicable to male subjects. The compensation operation implemented within module 240 improves the overall disease evaluation (e.g., those provided through visual feature point evaluation and machine learning evaluation) by leveraging its periodic variability information for male subjects. In fact, the CVMGD compensation module 240 operates on the fundamental principle that, within a given range of periodic fluctuations, a positive CAD evaluation for male subjects output from the predictive combination component 235 is likely to be a negative CAD evaluation in reality, and conversely, a negative CAD evaluation generated by the predictive combination component 235 for male subjects 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 periodic variability score of a male subject (e.g., calculated by module 300) is within a predetermined range (e.g., 0.0071 to 0.0079) and (ii) whether the intermediate CAD evaluation value for a given male subject, as output by module 235, is greater than or equal to a predetermined "threshold" (e.g., stored in module 240) by a factor X. If both conditions are met (e.g., a positive CAD evaluation for a male subject with a particular periodic variability signature), the CVMGD compensation module 240 is configured to output a final continuous CAD evaluation value for the male subject as the threshold minus a factor X (i.e., providing a negative CAD evaluation), where X is determined as the threshold minus the difference between the intermediate CAD evaluation score and the threshold. In other words, the CVMGD compensation module adjusts the intermediate CAD evaluation value determined via Equation 7 to a value equivalent to or of the same magnitude as the value obtained by subtracting X from the threshold. Conversely, the CVMGD compensation module 240 is configured to determine both (i) that the periodic variability score of a male subject (stored in module 300) is within the same predefined range of periodic variability scores, and (ii) that the intermediate CAD evaluation value is less than the value of quantity Y compared to its same predefined threshold. If both conditions are met (in the case of a negative CAD evaluation for a male subject with a specific periodic variability signature), the CVMGD compensation module 240 is configured to determine the final continuous CAD evaluation score as the threshold plus quantity Y (i.e., to provide a positive CAD evaluation), where Y is determined as the threshold plus the difference between the threshold and the intermediate CAD evaluation score. In other words, the CVMGD module adjusts the intermediate CAD evaluation value to a value equivalent to or of the same magnitude as the threshold plus Y. If the subject does not fall under any of the above scenarios, the subject's score will not be modified by module 240, and the original value will pass through unchanged.If modified, this change will embed information that, as previously defined, within a predetermined range of periodic fluctuations, a positive CAD evaluation for males (as an output from predictive combination component 235) is likely to be negative, and conversely, a negative CAD evaluation for males is likely to be positive.

[0094] In the example of module 240 relating to the embodiment in Figure 2, a male has (i) an intermediate CAD evaluation score of 0.14460082598168 and periodic fluctuation scores (maximum values ​​for ORTH1 and ORTH3) that fall within predefined (periodic fluctuation) thresholds of 0.0071 and 0.0079 (greater than 0.0071 and less than 0.0079), and (ii) a predefined (CAD evaluation) threshold of 0.13460082598168. Assuming a subject is involved, the CAD evaluation is modified by a compensation operation performed by module 240 so that the subject is assigned an intermediate CAD evaluation score of 0.124600825981680 (for example, calculated by subtracting the difference between the previous intermediate CAD evaluation score of 0.14460082598168 and the threshold of 0.13460082598168 from 0.13460082598168). The resulting final continuous CAD evaluation score is now below the threshold of 0.13460082598168 by the difference between the threshold and the input intermediate CAD evaluation value, so the intermediate CAD evaluation score (which was previously greater than the threshold) is adjusted to be less than the threshold. The modified score is then output from module 240. In fact, a positive CAD evaluation generated by the predictive combination component 235 for male subjects is output here as a negative evaluation.

[0095] The outputs of the signal quality evaluation module 300 and the CVGMD compensation component 240 are used in various ways in subsequent termination processes performed by various termination blocks or modules / components, which in system 200 of Figure 2 include 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 continuous CAD evaluation from the CVMGD compensation component 240 to provide a final binary CAD evaluation as output. In one implementation, if the final continuous CAD evaluation is greater than or equal to a threshold of 0.13460082598168, the subject is predicted to be CAD positive; otherwise, the subject is predicted to be CAD negative. The precision of the threshold (e.g., 0.13460082598168) has 14 significant digits because, when generated in this example, the threshold corresponds to a score belonging to a particular subject, and because it can be used to identify the threshold source during analysis and progression. In other embodiments, such threshold values ​​with various other precisions may be used.

[0097] The localization prediction generation module 255 uses 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) as inputs to output a CAD localization evaluation.

[0098] In some embodiments, the PST generation module 260 uses the unprocessed PSR signal 205 (via the signal quality evaluation system 300) to output a PST dataset / image. In other embodiments, the PST generation module 260 uses the PSR signal preprocessed by other modules in the evaluation system 110.

[0099] Figure 3 is a diagram showing an exemplary signal quality evaluation system 300 according to an exemplary embodiment, and Figure 4 is an operational flowchart relating to the 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 for signal quality evaluation that enables subsequent analysis. 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 located in the same computing device, or, as with all components of the system described throughout this disclosure, they may be located in separate computing devices that communicate with each other (e.g., directly connected or coupled to each other, or connected or coupled to each other in a communicative manner via a wired network, optical network, or wireless network). The network may be one or more different network types, or may consist of them, including local area networks (LANs) such as public switched telephone networks (PSTNs), cellular / mobile phone networks, wired or wireless Ethernet® networks, networks including near-field communication (NFC) or other radio frequency-based technologies and standards (e.g., Bluetooth®, Bluetooth® Low Energy, etc.), packet-switched networks (e.g., the Internet), etc. Although only one measurement system 380 is illustrated in Figure 3, there is no limit to the number of measurement systems 380 that may be supported. The signal quality evaluator 305 and the measurement system 380, as with all the components of the systems described throughout this disclosure, may each be implemented using one or more processors associated with any variety of computing devices, such as smartphones, smartwatches, desktop computers, server computers, mainframe computers, laptop computers, tablet computers, and set-top boxes (including any combination thereof). Other types of computing devices may be supported. A suitable computing device is illustrated as computing device 2200 in Figure 22.

[0101] The measurement system 380 may be any measurement system such as the measurement system 102, and the signal quality evaluator 305 may be implemented separately from the measurement system 102, or it 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 approximately 8 kHz), may be affected by noise that may originate from various sources. Such noise may affect the quality of signals acquired in various ways. For example, noise may adversely affect the performance of subsequent analyses, as described herein in relation to the clinical signs or disease symptoms of subjects or patients. This adverse effect on the performance of subsequent analyses may manifest in various ways. When processing is performed remotely (e.g., within a cloud service), real-time or near-real-time rejection of signals facilitates reacquisition or remeasurement of patients, reducing the inconvenience and cost to patients who would otherwise have to return to, for example, a clinic, hospital, or other clinical setting to reacquire signals. Furthermore, if subsequent analyses involve the generation of datasets / or images for physician interpretation (e.g., phase-space datasets / images), these images may not accurately represent the physiological state of the subject, potentially leading to misinterpretations and potentially delayed or incorrect diagnoses and / or treatments. If subsequent analysis involves an explicit quantitative assessment of a subject's given disease state, a negative run could result in an inaccurate quantification of the disease state, potentially leading to delayed or incorrectly withheld treatment, or unnecessary additional tests or interventions, which could be harmful to the patient. Furthermore, regardless of the type of analysis, noise can increase processing time and / or cost (in computational resources) to generate output. Therefore, it can be useful to identify and quantify noise, which may be removed, minimized, or otherwise processed, either entirely or partially (or, in some cases, together with one or more related signals), thereby eliminating or minimizing such adverse effects. Examples of noise relevant to this disclosure include, for example, power line interference, high-frequency noise, high-frequency noise bursts, (sudden) baseline shifts, and periodic fluctuations.For the purposes of this disclosure, any unwanted interference in the signals disclosed herein, regardless of their source, may be considered “noise.”

[0103] As can be seen from the exemplary methods shown in Figures 3 and 4, in the signal acquisition step 410, the measurement system 380 acquires an input signal 383, such as the PSR signal 205, and provides it to the signal quality evaluator 305. The input signal 383 may be a biophysical signal or a biophysical signal dataset acquired with respect to the subject. In the case of cardiac signals, resting chest physiological signals of the subject may be collected 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, arranged along three orthogonal axes corresponding to the ORTH1, ORTH2, and ORTH3 channels as described above. Although electrodes as part of the non-invasive measurement system 102 can acquire phase gradient biophysical signals (from which set 108 is derived, as described above) without using ionizing radiation, contrast agents, motion, or pharmacological stressors, in some embodiments, the biophysical signals of interest can nevertheless be used in combination with such protocols or instruments. In some embodiments, the non-invasive measurement system 102 samples at approximately 8 kHz for a duration of approximately 30 seconds to approximately 1400 seconds, preferably for a duration of approximately 210 seconds. The acquired data points are transferred to the evaluation system 110 as part of the dataset 108 and evaluated by an internal analysis engine employing, for example, a machine learning algorithm / predictor. Other electrode sets and electrophotographic 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 a quality evaluation by using 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, and the periodic fluctuation noise module 360, or any of them, or all of them, in combination with the determination module 310 to generate an output such as output 386. The signal quality evaluator 305, and / or one or more of the power line interference module 320, high-frequency noise module 330, high-frequency noise burst module 340, sudden movement module 350, and periodic fluctuation noise module 360, and / or the determination module 310 may reside on or inside a device or apparatus that houses or otherwise includes the measurement system 102 or the signal quality evaluation system 300, or may be located locally and remotely (e.g., a server / processor, software, and services, etc., that exist and / or operate in a “cloud” and communicate with a local server / processor via a network such as the Internet), or it may be a hybrid system / hybrid configuration in which part of the quality evaluation operation is performed on the device and part of the quality evaluation operation is performed remotely. An example of a suitable device or apparatus is shown in Figure 22 as a computing device 2200.

[0105] In step 430, output 386 is provided to the measurement system 380 and the user. In one implementation, 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 and terminal processing components described with reference to Figure 2).

[0106] In one implementation, the input signal 383 is the unmodified PSR signal (channel data from ORTH1, ORTH2, and ORTH3, analyzed from a PSR file downloaded from the PSDR (Phase Signal Data Repository)). As described above, the data from channel "ORTH1" corresponds to a series of bipolar acquired channel data recorded by the phase-space recorder from electrodes 114 positioned along or near a single orthogonal axis that passes through the subject's body from left to right. The data from channel "ORTH2" corresponds to a second series of bipolar acquired channel data recorded by the phase-space recorder from two other electrodes 114 positioned along or near a second orthogonal axis that passes through the subject's body from top to bottom. The data from channel "ORTH3" corresponds to a third bipolar acquisition channel data series recorded by a phase-space recorder from two additional electrodes 114 positioned along or near a third orthogonal axis that passes through the subject's body from front to back. The signals from ORTH1, ORTH2, and ORTH3, and their corresponding datasets, can be arranged, for example, on mutually orthogonal axes in phase-space coordinates.

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

[0108] As will be further explained with respect to Figures 3 and 5-7, the power line interference module 320 detects or otherwise determines power line interference noise (for example, noise from 60 Hz power lines (a frequency commonly used in Canada, South Korea, Taiwan, the United States, and some parts of Japan, etc.) and their harmonics, and noise from 50 Hz power lines (a frequency commonly used in China, France, Germany, Hong Kong, India, Italy, Switzerland, the United Kingdom, and some parts of Japan) and their harmonics). The determined power line interference noise is provided to the determination module 310 for processing, such as processing as further described herein.

[0109] As will be further explained with respect to Figures 3 and 8-10, the high-frequency noise module 330 detects or otherwise determines excessive signal frequency components (for example, in one implementation, frequency components greater than 170 Hz, and such frequency components greater than 170 Hz may not necessarily be 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 as further described herein.

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

[0111] As will be further explained with respect to Figures 3 and 14-16, the transient motion module 350 detects or otherwise determines extreme baseline fluctuations localized to a specific segment of the signal that are large enough to cause signal distortion. The output of the transient motion module 350 is provided to the determination module 310 for processing, such as the processing described further herein.

[0112] As will be further explained with respect to Figures 3 and 17-19, the periodic fluctuation noise module 360 ​​provides asynchronous noise quantification with respect to the cardiac cycle, which may include potentials generated by skeletal muscle activation. The output of the periodic fluctuation noise module 360 ​​is provided to the determination module 310 for processing, such as processing as further described herein. The concepts described herein can be applied to quantify asynchronous noise with respect to other periodic physiological signals outside of cardiac conditions.

[0113] In one implementation, the evaluation described herein is performed at the channel level, for example, independently on the ORTH1 channel, ORTH2 channel, and ORTH3 channel, and then combined in a subsequent stage of processing.

[0114] Figure 5 is an operational flowchart relating to the 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 power line noise intrusion, where a larger value indicates greater intrusion.

[0115] In step 510, the power line coefficients are calculated by switching from the time domain to the frequency domain using the Fast Fourier Transform (FFT). In step 520, the presence of a deflection or peak in the FFT periodogram is quantified, for example, at 60 Hz. In some embodiments, the method includes determining the base local frequency energy through the average decibel values ​​at 55 Hz–58 Hz and 62 Hz–65 Hz (e.g., base = absolute value (mean value (power (55 ≤ frequency ≤ 58 or 62 ≤ frequency ≤ 65))), thereby providing a baseline power for detecting whether a peak occurring in the middle of that range (e.g., 58 Hz–62 Hz). The method then determines the maximum power line energy through the maximum decibel value at 58 Hz–62 Hz (e.g., peak height = base - absolute value (mean value (power (58 ≤ frequency ≤ 62))), thereby quantifying the peak at 58 Hz–62 Hz. The method then determines the ratio of the peak height on the base to the base; that is, it determines the following:

number

[0116] In step 530, the power line coefficients are further extended to include the sum of the harmonics (from 60 Hz) at 180 Hz and 300 Hz, calculated using the same methodology as described above for the power line coefficients at 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 coefficients for each of the ORTH1, ORTH2, and ORTH3 channels, with the basic name "Power Line Coefficients" (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 coefficients) as a score compared to a threshold. In some embodiments, the power line coefficient score is determined as follows:

number

[0117] In this specification, diagrams (plots) are used to aid in the explanation of different types of noise and to share common types of labels (e.g., Figures 6, 7, 9, 10, 12, 13, 15, 16, 18, and 19), where time is along the horizontal axis (time points in units of samples), reflected here as the number of data points in an 8 kHz signal (8000 samples / second are acquired), and amplitude is along the vertical axis. In these figures, amplitude is shown in mV, although some implementations may use normalized amplitude by subtracting the mean of the signal and dividing by the standard deviation (thus converting the signal to a z-score, for example, the number of standard deviations from the mean of the data points, with the sign indicating directionality).

[0118] For example, Figure 6 shows an observable characteristic, such as the maximum power line interference characteristic, in the cardiac context of the power line interference evaluation operation of Figure 5, according to another exemplary embodiment. Figure 6 shows the maximum power line coefficient at the channel level. Power line interference can be clearly observed between ventricular depolarization signals as periodic oscillations with an amplitude range of -9.15 mV to -9.45 mV.

[0119] Figure 7 is a figure 700 showing the observable characteristics of power line interference at a threshold in the power line interference evaluation operation method of Figure 5, for example, according to an exemplary embodiment. Figure 7 shows the narrowly accepted power line coefficients, for example, a first coefficient that would be accepted, and those larger than this first coefficient are rejected. That is, in this data, it was found that the power line coefficient for 60 Hz is accepted, but the power line coefficients for 180 Hz and 300 Hz are not accepted.

[0120] Figure 8 is an operational flowchart for an implementation of an exemplary method 800 for evaluating high-frequency noise, according to an exemplary embodiment. The high-frequency noise score is first calculated in 810 by performing a steady-state wavelet transform (SWT). Note that the SWT is functionally similar to the Fast Fourier Transform, except that it allows for the localization of frequencies in time at the expense of frequency detail. In step 820, the energy at levels 9 through 13, corresponding to components greater than 170 Hz, is preserved, and in step 830, an inverse SWT is performed. The results of these transforms are the high-frequency components returned to the time domain (which would be visible if their intermediate outputs were plotted).

[0121] In step 840, the signal-to-noise ratio (SNR) is calculated as a high-frequency noise score by comparing the original signal with the noise extracted by the SWT in the time domain. The output from this evaluation has the basic name "High-Frequency Noise vs. Signal Ratio," which is modified only to indicate the source channel.

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

[0123] Figure 9 shows an observable characteristic of the maximum high-frequency noise in the cardiac condition, for example, in the high-frequency noise evaluation operation of Figure 8, according to an exemplary embodiment. Here, the high-frequency noise can be seen as a thick, dark line, partially enclosed by box 902 in Figure 9. This noise in Figure 9 starts with an amplitude range of approximately 3mV to 4mV and tends to decrease over time, with a slow baseline fluctuation, from approximately -2mV to 4mV.

[0124] Figure 10 shows high-frequency noise at a threshold for the high-frequency noise evaluation operation of Figure 8, according to an exemplary embodiment in the context of cardiac conditions. Here, the signal is just acceptable. The x-axis time scale is smaller than that of Figure 9, and the phase waveforms are clearly identifiable (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., "T wave") and before atrial depolarization ("P wave"), as shown, for example, in box 1010. Such noise occurs in all cycles.

[0125] Figure 11 is an operational flowchart of an exemplary implementation of 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 Figure 8, but instead is localized to a 1-second segment. The value of this score, along with sudden or extreme baseline fluctuations (further described herein), cannot remove the signal from subsequent analysis on its own, but occurs when the signal length is insufficient (e.g., at least 16 consecutive seconds, and the same duration across all channels) without a high-frequency noise burst or sudden baseline shift.

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

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

[0128] In one implementation, the output from this evaluation consists of two scores that are modified to indicate the source data channel. For example, the scores may be output as "median high-frequency signal energy" (representing the median high-frequency energy overall) and "high-frequency noise burst coefficient" (representing the 1-second energy for any window larger than four times the median high-frequency signal energy). If there are multiple such windows, the maximum value is returned.

[0129] Figure 12 is a figure showing an observable characteristic, the maximum high-frequency noise burst 1210, in the high-frequency noise burst evaluation operation of, for example, Figure 11, according to an exemplary embodiment. In this case, the high-frequency noise burst 1210 is characterized as an impulse that lasts for several data points (e.g., less than 1 millisecond). As with the other figures (Figures 6, 7, 9, 10, 13, 15, 16, 18, and 19), the unit on the x-axis is samples and the unit on the y-axis is mV (unnormalized).

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

[0131] Figure 14 is an operational flowchart for the implementation of Method 1400 for evaluating sudden baseline shifts in an exemplary embodiment in a cardiac context. In this example, a baseline shift is defined as “sudden” if the baseline of a 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 of “sudden” in this context may be established depending on the type of biophysical signal acquired and analyzed, the patient’s physiological state, and other factors as needed.

[0132] Step 1410 determines the baseline of the input signal within a 1-second window. Step 1420 determines the baseline of the signal within the next 1-second window. Step 1430 determines the score based on the baseline shift between the two windows.

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

[0134] Figure 15 is an exemplary figure illustrating the observable characteristic of the maximum sudden shift with respect to the subject in the sudden baseline shift evaluation procedure, for example, in Figure 14, according to an exemplary embodiment in the cardiac context. Here, the phase signal is initially stable at an amplitude of 100 mV, but subsequently, this baseline value drops sharply, first by about 20 mV, and then to around 30 mV. During these sudden baseline changes, the phase signal is drastically reduced or absent.

[0135] Figure 16 is an exemplary Figure 1600 illustrating an observable characteristic of approximately 50% sudden baseline shift in an exemplary embodiment of the cardiac situation, for example, in the sudden baseline shift evaluation procedure of Figure 14. This is a more typical example of sudden baseline shift, illustrated here as 1610, which illustrates an upward shift in signal amplitude followed immediately by a similar downward shift, and this shift in signal amplitude results in the ventricular depolarization event at the peak of the shift having an amplitude approximately 50% larger than the ventricular depolarization event in its 1-second window prior to it.

[0136] Figure 17 is an operational flowchart relating to the implementation of method 1700 for evaluating periodic fluctuations in an exemplary embodiment in the context of the heart. Examination of phase-periodic fluctuations (in the cardiac system, in other physiological systems, or in combination thereof) reveals asynchronous noise, which can quantify the presence of muscle noise artifacts and other types of noise that lack alignment with the phase period.

[0137] Periodic fluctuation noise can be calculated using the exemplary technique shown in Figure 17, which detects components with similar amplitudes within the frequency band of the phase signal.

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

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

[0140] In step 1740, this technique creates a final periodic variation score for the channel by compressing all the resulting differences over the detected phase period.

[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 is excluded from this calculation because it was found to have the largest final periodic variation score for more than two-thirds of the time compared to ORTH1 and ORTH3, but for a much larger amount of time than for one-third (which is statistically expected if each of the three channels has an equal probability of having the largest final periodic variation). Therefore, including the final periodic variation score for the signal obtained from the ORTH2 channel would disproportionately affect or promote 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 under certain circumstances.

[0142] In human subjects with cardiac conditions, the ORTH2 vector typically extends from directly below the subject's left clavicle to directly below the end of the rib cage, and without being bound by theory, two potential signal acquisition problems arise that could explain a larger associated final periodic variability score and thus a larger associated noise level compared to other channels. Firstly, the termination of the ORTH2 vector directly below the rib cage is located above 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 higher BMI typically indicates an excess of abdominal fat, which alters the impedance between the two electrodes used for signal acquisition. Secondly, the ORTH2 vector is perpendicular to the striated muscle of the left pectoral muscle, making it more likely that noise related to the pectoral muscle will intrude into and / or affect the signal. Muscle noise is generated by contraction, but contraction does not necessarily mean movement. For example, isometric contraction is a static contraction of muscle where there is no visible movement in the joint angle.

[0143] Figure 18 is an observable characteristic, maximum periodic variability noise, in the periodic variability evaluation procedure of Figure 17, for example, in an exemplary embodiment of the cardiac situation. The noise is most visible between phase periods, in the period after the offset of a ventricular repolarization event and before an atrial depolarization event, and appears similar to external phase waveforms (one or more additional ventricular repolarization events, etc.). An example of this 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., QRS waveforms are illustrated as "spikes").

[0144] Figure 19 shows an observable characteristic of smaller periodic noise in the periodic fluctuation evaluation operation of Figure 17, for example, in an exemplary embodiment of the cardiac situation. This figure shows a smaller level of noise intrusion compared to the noise signature of Figure 18, for example. Other differences are also seen here, for example, the noise has a relatively low frequency and a relatively large amplitude range, the latter resulting in a more impulse-like signature in the example of Figure 19.

[0145] Figure 20 is an operational flowchart illustrating an implementation of the signal quality evaluation method 2000 according to an exemplary embodiment. In particular, the score is evaluated in the signal usability workflow described with respect to Figure 20.

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

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

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

[0149] Steps 2020, 2025, and 2030 perform a sudden movement test (collectively, "Test #3") to determine whether a clean signal segment is available due to sudden movement and high-frequency noise bursts. If one or more of the partial tests of Test #3 in each of steps 2020, 2025, and 2030 indicate that such a clean signal segment is unavailable, the input signal is rejected in step 2060. Otherwise, the input signal is considered to have passed each of the tests in steps 2020, 2025, and 2030, and a binary number indicating that the input signal is Test #3 and passed is provided for the Boolean "logical AND" operator.

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

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

[0152] Therefore, with respect to any given signal, (1) if any channel of the patient or subject fails the power line interference criterion test #1 in step 2010, the patient's input signal is rejected; (2) if any channel of the subject fails the high-frequency noise criterion, the subject's input signal is rejected; (3) if the patient's channels ORTH1, ORTH2, and / or ORTH3 fail the periodic fluctuation threshold, the patient's input signal is rejected; and (4) if a sudden shift or high-frequency noise burst is detected in the patient's input signal, find at least one 16-second segment of the signal in which no sudden shift or noise burst is detected. An attempt is made to verify whether the segment is the same across all channels (for example, all channels are clean between the segments), and if no such window is found, the patient's input signal is rejected; otherwise (i.e., if such a window is found), the patient's input signal is processed using the found window, and (5) if all channels of the patient's input signal pass all criteria, the subject's input signal is processed normally, which is shown in Figure 20 as the "Boolean AND" operator 2040 (i.e., all tests must pass).

[0153] Table 1 shows an exemplary table of tests and thresholds in one implementation. [Table 1]

[0154] In North America, for example, 60Hz is typically used for detecting power line frequencies, but in other regions such as China, the EU, and India, 50Hz is typically used instead, so adjustments to scores targeting those specific frequencies may be desirable.

[0155] Figures 21A and 21B show an architecture and dataflow diagram 2100 relating to an exemplary signal quality evaluation component according to an exemplary embodiment.

[0156] Failure in any of the tests, in illustrative examples, does not preclude the subsequent implementation of the evaluation system 200. Rather, it should be noted that failure may be reported through the following mechanisms, and a PST evaluation may be attempted.

[0157] The input may include unmodified ORTH1, ORTH2, and ORTH3 data analyzed from phase-space recorder files.

[0158] The output may be passed to Data Transfer APIs (DTAPI) and a Report Database (RD). In one implementation, the legacy fields "Noise Volume" and "Average Noise Level" are reused to maintain backward compatibility within the DTAPI and RD components. In some embodiments, the Report Database is configured to store the Noise Volume parameter as a Periodic Fluctuation Score if all other signal quality evaluation tests are passed. If any test (other than Periodic Fluctuation) is failed, a flag of 10000 is used to indicate that condition. In one implementation, Periodic Fluctuation may have a threshold of 0.0106.

[0159] For example, in some embodiments, the average noise level stores a state code having the following possible states, as provided in Table 2. [Table 2-1] [Table 2-2]

[0160] Figure 22 shows an exemplary computing environment that can implement exemplary embodiments and aspects according to exemplary embodiments, for example, an evaluation system 110 and a signal quality evaluator 305. The computing device environment is merely an example of a suitable computing environment and is not intended to imply any limitations on the scope or functionality of use.

[0161] Numerous 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 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 similar.

[0162] Computer-executable instructions, such as program modules, that are executed by a computer, may be used. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. A distributed computing environment may be used in which tasks are performed by remote processing devices linked via a communication network or other data transmission medium. In a distributed computing environment, program modules and other data may be located in local and remote computer storage media, including memory storage devices.

[0163] Referring to Figure 22, an exemplary system for implementing the embodiments 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 memory 2204. Depending on the detailed configuration and type of computing device, memory 2204 may be volatile (e.g., random access memory (RAM)), non-volatile (e.g., read-only memory (ROM), flash memory), or some combination of the two. This most basic configuration is illustrated by the dashed line 2206 in Figure 22.

[0164] The computing device 2200 may have additional features / functions. For example, the 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 Figure 22 by removable storage 2208 and non-removable storage 2210.

[0165] The computing device 2200 typically includes various computer-readable media. These computer-readable media can be any available media accessible to the device 2200, and include both volatile and non-volatile media, as well as both removable and non-removable media.

[0166] Computer storage media include volatile and non-volatile, removable and non-removable media, implemented in any way or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Memory 2204, removable storage 2208, and non-removable storage 2210 are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory 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 media that can be used to store desired information and that can be accessed by the computing device 2200. Any such computer storage media may be part of the computing device 2200.

[0167] The computing device 2200 may include one or more communication connections 2212 that enable the device to communicate with other devices. The 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., either alone or in combination. It may also include one or more output devices 2216, such as a display, speaker, printer, vibration mechanism, etc. All of these devices are well known in the art and do not need to 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, where appropriate, in combination thereof. Exemplary types of usable hardware components 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. Methods and apparatus relating to the subject matter of this disclosure, or particular aspects or parts thereof, may take the form of program code (i.e., instructions) embodied in tangible media such as floppy disks, CD-ROMs, hard drives, or any other machine-readable storage media such as a computer, which becomes an apparatus for carrying out the subject matter of this disclosure when the program code is read and executed by such a machine.

[0169] While exemplary implementations may refer to utilizing embodiments of the subject matter of this disclosure in the context of one or more autonomous computer systems, the subject matter is not limited thereto and may rather be implemented in connection with any computing environment, such as a network or distributed computing environment. Furthermore, embodiments of the subject matter of this disclosure may be implemented on or across multiple processing chips or devices, and storage may similarly be effective 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 actions, it will be understood that the subject matter defined in the attached claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as exemplary forms for implementing the claims.

[0171] Further examples of various processes that may be used in conjunction with the illustrated methods and systems are given 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 Electrocardiographic Method for Estimating U.S. Patent No. 9,737,229, titled "Mammalian Cardiac Chamber Size and Mechanical Function", U.S. Patent No. 10,039,468, titled "Noninvasive Electrocardiographic Method for Estimating Mammalian Cardiac Chamber Size and Mechanical Function",U.S. Patent No. 9,597,021, titled "Method for Estimating Glucose, Glycosylated Hemoglobin and Other Blood Constituents"; U.S. Patent No. 9,968,265, titled "Method and System for Characterizing Cardiovascular Systems From Single Channel Data"; U.S. Patent No. 9,910,964, titled "Methods and Systems Using Mathematical Analysis and Machine Learning to Diagnose Disease"; U.S. Patent Application Publication No. 2017 / 0119272, titled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition"; PCT International Application Publication No. WO2017 / 033164, titled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition"; "Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve U.S. Patent Application Publication No. 2018 / 0000371, titled "Estimation," PCT International Application Publication No. WO2017 / 221221, titled "Non-invasive Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation," U.S. Patent No. 10,292,596, titled "Method and System for Visualization of Heart Tissue at Risk," and "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, "Method and System to PCT International Patent Application Publication No. PCT / IB2018 / 060709, titled "Assess Disease Using Phase Space Tomography and Machine Learning," U.S. Patent Application Publication No. 2019 / 0384757, filed on June 18, 2019, titled "Method and Systems to Quantify and Remove Asynchronous Noise in Biophysical Signals," U.S. Patent Application Publication No. 2019 / 0365265, titled "Method and System to Assess Pulmonary Hypertension Using Phase Space Tomography and Machine Learning," and "Method and System to Assess Disease," filed concurrently with this specification.U.S. Patent Application No. __ / __ / __ titled "Using Phase Space Tomography and Machine Learning" (with attorney reference number 10321-034us1, claiming priority to U.S. Provisional Patent Application No. 62 / 784,984 and U.S. Provisional Patent Application No. 62 / 835,869), U.S. Patent Application No. __ / __ / __ titled "Method and System to Configure and Use Neural Network To Assess Medical Disease" (with attorney reference number 10321-037pv1, claiming priority to U.S. Provisional Patent Application No. 62 / 784,925), U.S. Patent Application No. 15 / 653,433 titled "Discovering Novel Features to Use in Machine Learning Techniques, such as Machine Learning Techniques for Diagnosing Medical Conditions", "Discovering Genomes to Use in Machine Learning This is described in U.S. Patent Application No. 15 / 653,431, entitled “Techniques”, U.S. Patent Application No. __ / __ / __, entitled “Method and System to Assess Disease Using Dynamical Analysis of Cardiac and Photoplethysmographic Signals” (with attorney reference number 10321-041pv1, claiming priority of the application), and U.S. Patent Application No. __ / __ / __, entitled “Method and System to Assess Disease Using Dynamical Analysis of Biophysical Signals” (with attorney reference number 10321-040pv1, claiming priority of the application), each of which is incorporated herein by reference in its entirety.

[0172] Unless otherwise explicitly stated, none of the methods described herein are intended to be construed as requiring their steps to be performed in a specific order. Therefore, if a claim relating to a method does not actually describe the order in which the steps should be followed, or if the steps are not explicitly stated in another aspect of the claim or description to be limited to a specific order, no order is intended to be inferred in any way. This also applies to any possible implicit grounds for interpretation, including logical matters relating to the structure or flow of the steps, plain meanings derived from grammatical structure or punctuation, and the number or type of embodiments described in the specification.

[0173] Although methods and systems have been described in relation to specific embodiments and examples, the scope is not intended to be limited to the specific embodiments described herein, as the embodiments herein are intended to be illustrative rather than restrictive in all respects.

[0174] The methods, systems, and processes described herein may be used to generate stenosis and FFR outputs for use in connection with procedures such as the placement of vascular stents into blood vessels, such as arteries, of living (e.g., human) subjects, and for use in connection with other interventional and surgical systems or processes. In one embodiment, the methods, systems, and processes described herein may be configured to use the FFR / stenosis output to intraoperably determine and / or modify the number of stents to be placed in a living (e.g., human) body, including, in particular, the optimal positions for placement within a given blood vessel.

[0175] Other biophysical signals that can be analyzed whole or in part using the exemplary methods and systems include, but are not limited to, electrocardiogram (ECG) datasets, electroencephalogram (EEG) datasets, gamma-synchronized 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] Exemplary analyses can be used for the diagnosis and treatment of cardiac conditions and symptoms and / or neurological conditions and symptoms, and such assessments can be applied to the diagnosis and treatment (including surgical, minimally invasive, and / or pharmacological) of any condition or symptom involving biophysical signals in any relevant system of the body. An example in cardiac conditions is the diagnosis of CAD and the treatment of any number of therapies, either alone or in combination, such as stent placement in the coronary arteries, atherectomy, angioplasty, prescription of drug therapy, and / or prescription of exercise, nutrition, or other lifestyle changes. Other cardiac conditions or symptoms that can be diagnosed include, for example, arrhythmias, 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 thrombosis, and pulmonary hypertension due to other diseases or disorders such as blood), and other cardiac conditions, symptoms, and / or diseases. Non-limiting examples of neurological 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 disorder (including Asperger's syndrome), attention deficit hyperactivity disorder, Huntington's disease, muscular dystrophy, depression, bipolar disorder, brain / spinal tumors (malignant and benign), motor disorders, cognitive impairments, speech disorders, various psychoses, brain / spinal / nerve injuries, chronic traumatic encephalopathy, cluster headaches, and hemiplegia. This includes headaches, neurological disorders (including various forms such as peripheral neuropathy), phantom limb / phantom limb pain, chronic fatigue syndrome, acute and chronic pain (including lower back pain, spinal surgery failure syndrome, etc.), motor disorders, anxiety disorders, symptoms caused by infection or foreign substances (e.g., Lyme disease, encephalitis, rabies), somnolencephalitis and other sleep disorders, post-traumatic stress disorder, neurological symptoms / effects associated with stroke, aneurysms, hemorrhagic injuries, etc., tinnitus and other auditory disorders / symptoms, and visual disorders / symptoms.

Claims

1. A method for obtaining a biophysical signal dataset for clinical analysis, wherein the method is The processor acquires a biophysical signal dataset relating to a subject for measurement, the biophysical signal dataset being acquired via one or more surface probes of a non-invasive measurement system on one or more corresponding channels, and over an acquisition duration suitable for subsequent evaluation, the acquisition duration being predetermined, dynamically determined, or set by the user. Determining a periodic variation score using the biophysical signal dataset by one or more cloud-based services or systems, wherein the periodic variation score is calculated for each of the one or more corresponding channels, by detecting all phase periods within the selected channel, creating a template phase period that represents all the detected phase periods, comparing all the detected phase periods with the template phase period and quantifying the difference, and compressing all the resulting differences across all the detected phase periods to create a final periodic variation score for the selected channel, and determining the largest periodic variation score among those calculated for each channel as the periodic variation score for the biophysical signal dataset; The aforementioned one or more cloud-based services or systems determine that the entire biophysical signal dataset fails quality assessment based on the periodic fluctuation score of the biophysical signal dataset exceeding a periodic fluctuation noise threshold, In response to the determination that the entire biophysical signal dataset failed the quality assessment, The rejection of any analysis of the biophysical signal dataset by one or more of the aforementioned cloud-based services or systems, and To instruct the non-invasive measurement system to reacquire the biophysical signal dataset. A method that includes performing the action.

2. The method according to claim 1, further comprising outputting one or more of a visual indicator, an audio indicator, a vibration indicator, and a report regarding an evaluation that failed in the non-invasive measurement system, wherein the output is performed simultaneously with or substantially simultaneously with the measurement.

3. The method according to claim 1 or 2, further comprising transmitting the acquired biophysical signal dataset via a network by the processor for remote clinical analysis after evaluation of non-rejection, i.e., after evaluation of acceptance, with respect to the acquired biophysical signal dataset.

4. The measurement system acquires a voltage gradient signal on one or more channels using one or more acquisition circuits, wherein the voltage gradient signal is acquired at a frequency greater than approximately 1 kHz. The method according to any one of claims 1 to 3, further comprising generating the acquired biophysical signal dataset from the acquired voltage gradient signal using the one or more acquisition circuits.

5. At least the first surface probe should be positioned along the first axis that passes through the subject's body from left to right, At least the second surface probe is positioned along a second axis that passes through the subject's body from top to bottom, The method according to any one of claims 1 to 4, further comprising positioning at least a third surface probe on a third axis that passes through the body of the subject from front to back, wherein the first axis, the second axis, and the third axis are mutually orthogonal axes.

6. The method according to any one of claims 1 to 5, wherein if the power line interference parameter for any one of the one or more channels fails to meet the power line interference conditions, the acquired biophysical signal dataset or the evaluation portion is rejected.

7. The method according to any one of claims 1 to 6, wherein if the high-frequency noise parameter related to high-frequency noise contamination with respect to any one of the one or more channels fails to meet the high-frequency noise conditions, the acquired biophysical signal dataset or the evaluation portion is rejected.

8. The method according to any one of claims 1 to 7, wherein if the noise burst parameter related to high-frequency noise burst contamination with respect to any of the one or more channels fails to meet the noise conditions, the acquired biophysical signal dataset or the evaluation portion is rejected.

9. The method according to any one of claims 1 to 8, wherein if the sudden movement parameter related to sudden movement contamination with respect to any one of the one or more channels fails to meet the sudden movement conditions, the acquired biophysical signal dataset or the evaluation portion is rejected.

10. The method according to any one of claims 1 to 9, wherein if an asynchronous noise parameter that may include skeletal muscle contamination with respect to any of the one or more channels fails to meet the asynchronous noise conditions, the acquired biophysical signal dataset or the evaluation portion is rejected.

11. The power line coefficients related to the aforementioned power line interference parameters are, The processor performs a Fourier transform on the acquired biophysical signal dataset or a portion thereof. The method according to any one of claims 1 to 10, wherein the processor determines the maximum value of the power line energy in multiple frequency ranges.

12. The method according to any one of claims 1 to 11, wherein the evaluation serves as a gating stage for subsequent analysis of the subject with respect to coronary artery disease or pulmonary hypertension.

13. The method according to any one of claims 1 to 12, wherein the biophysical signal dataset to be received includes a cardiac signal dataset.

14. The method according to any one of claims 1 to 13, wherein the biophysical signal dataset is generated in near real-time with respect to the acquisition of biophysical signals.

15. The method according to any one of claims 1 to 14, wherein the biophysical signal is obtained from a sensor in a smart device or in a handheld medical diagnostic device.

16. The method according to any one of claims 1 to 5, wherein the biophysical signal dataset includes broadband phase gradient cardiac signal data acquired simultaneously from a plurality of surface electrodes placed on a body surface adjacent to the heart of the subject.

17. The method according to any one of claims 1 to 16, further comprising the processor generating a notification that the acquisition of the biophysical signal dataset was unsuccessful.

18. The method according to any one of claims 1 to 17, wherein the notification prompts the user to subsequently perform the acquisition of the biophysical signal dataset.

19. The method according to any one of claims 1 to 18, further comprising the processor causing the received biophysical signal dataset to be transmitted over a network to an external analysis system, wherein the analysis system is configured to analyze the received biophysical signal data for the presence or degree of a disease or clinical symptom.

20. It is a system, One or more processors, A system comprising: a memory having instructions stored on one or more processors, wherein the execution of the instructions by the one or more processors causes the one or more processors to execute the method according to any one of claims 1 to 19.

21. A non-temporary computer-readable medium having stored instructions, wherein the execution of the instructions by one or more processors causes one or more processors to execute the method according to any one of claims 1 to 19.

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