阅读说明：本技术 用于自动量化信号质量的方法和系统 (Method and system for automatically quantizing signal quality ) 是由 M·加勒特 T·W·F·伯顿 S·拉姆昌达尼 A·杜姆拉 于 2019-12-23 设计创作，主要内容包括：提供了用于量化采集的信号的质量的系统和方法用于评估以及用于选通采集的信号以便后续分析。采集信号并实时地确定采集是否有问题(例如,采集的信号是可接受的还是不可接受的,采集信号的质量是否足以用于后续评估)。如果有问题,则经由在此描述的系统和方法提供输出以指示需要重新执行信号采集(例如,如果采集的信号是不可接受的,则拒绝采集的信号并采集新的信号)。(Systems and methods for quantifying the quality of acquired signals are provided for evaluating and for gating acquired signals for subsequent analysis. The signals are acquired and a determination is made in real time as to whether the acquisition is problematic (e.g., whether the acquired signals are acceptable or unacceptable, whether the quality of the acquired signals is sufficient for subsequent evaluation). If there is a problem, an output is provided via the systems and methods described herein to indicate that signal acquisition needs to be re-performed (e.g., if the acquired signal is unacceptable, the acquired signal is rejected and a new signal is acquired).)

1. A method of acquiring a biophysical signal dataset for clinical analysis, the method comprising:

obtaining, by a processor, a biophysical signal dataset, or a portion thereof, of a subject for measurement, wherein the biophysical signal dataset, or portion thereof, is acquired via one or more surface probes of a non-invasive measurement system, through one or more respective channels, and is acquired over an acquisition time period suitable for subsequent evaluation, wherein the acquisition time period is predefined, dynamically determined, or set by a user;

determining, by the processor and/or remotely by one or more cloud-based services or systems, one or more signal quality parameters of the obtained biophysical signal dataset, wherein at least one of the one or more signal quality parameters is selected from the group consisting of: a power line interference parameter related to power line noise pollution, a high frequency noise parameter related to high frequency noise pollution, a noise burst parameter related to high frequency noise burst pollution, a sudden movement parameter related to sudden movement pollution, and an asynchronous noise parameter related to skeletal muscle pollution or heart cycle variability; and

rejecting, by the processor, the obtained biophysical signal dataset, or the evaluated portion thereof, when the one or more signal quality parameters fail the noise quality evaluation performed on the one or more signal quality parameters.

2. The method of claim 1, further comprising:

outputting, at the non-invasive measurement system, one or more of a visual indicator, an audio indicator, a vibration indicator, and a report that failed to evaluate, wherein the outputting is performed simultaneously or nearly simultaneously with the measuring.

3. The method of claim 1 or 2, further comprising:

after a non-repudiation or an acceptance evaluation of the obtained biophysical signal dataset, transmitting, by the processor, the obtained biophysical signal dataset over a network for remote clinical analysis.

4. The method of any of claims 1-3, further comprising:

acquiring, by one or more acquisition circuits of the measurement system, voltage gradient signals on the one or more channels, wherein the voltage gradient signals are acquired at a frequency greater than about 1 kHz; and

generating, by the one or more acquisition circuits, the obtained biophysical data set from the acquired voltage gradient signals.

5. The method of any of claims 1-4, further comprising:

placing at least a first surface probe at a first axis of the subject passing left to right through the subject's body;

placing at least a second surface probe at a second axis of the subject that passes through the subject's body from top to bottom; and

placing at least a third surface probe at a third axis passing through the subject's body from front to back, wherein the first axis, the second axis, and the third axis are mutually orthogonal axes.

6. The method of any one of claims 1-5, wherein the obtained biophysical signal dataset or the evaluated portion thereof is rejected when the power line interference parameter of any of the one or more channels does not comply with the power line interference condition.

7. The method of any one of claims 1-6, wherein the obtained biophysical signal dataset, or the evaluated portion thereof, is rejected when a high frequency noise parameter, associated with high frequency noise pollution, of any of the one or more channels does not comply with a high frequency noise condition.

8. The method of any one of claims 1-7, wherein the obtained biophysical signal dataset, or the evaluated portion thereof, is rejected when a noise burst parameter associated with high frequency noise burst pollution by any one of the one or more channels does not comply with a noise condition.

9. The method of any one of claims 1-8, wherein the obtained biophysical signal dataset, or the evaluated portion thereof, is rejected when a sudden movement parameter associated with sudden movement contamination of any one of the one or more channels does not comply with a sudden movement condition.

10. The method of any one of claims 1-9, wherein the obtained biophysical signal dataset, or the evaluated portion thereof, is rejected when an asynchronous noise parameter of any of the one or more channels, which may include skeletal muscle contamination or heart cycle variability, does not comply with an asynchronous noise condition.

11. The method according to any of claims 1-10, wherein the power line coefficients related to the power line interference parameters are determined by:

performing, by the processor, a Fourier transform on the obtained biophysical signal dataset or portion thereof; and

determining, by the processor, a maximum power line energy in a plurality of frequency ranges.

12. The method of any one of claims 1-11, wherein the assessment is a gated phase for subsequent analysis of the subject for coronary artery disease or pulmonary arterial hypertension.

13. The method of any one of claims 1-12, wherein the received biophysical signal dataset comprises a cardiac signal dataset.

14. The method of any one of claims 1-13, wherein the biophysical signal dataset is generated in near real-time as biophysical signals are acquired.

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

16. The method of any one of claims 1-5, wherein the biophysical signal dataset comprises wideband phase gradient cardiac signal data simultaneously captured from a plurality of surface electrodes placed on a body surface proximate the subject's heart.

17. A method of rejecting an acquired biophysical signal, the method comprising:

receiving, by a processor, a biophysical signal dataset of a subject;

comparing, by the processor and/or remotely by one or more cloud-based services or systems, the received biophysical signal dataset to at least one of: power line interference, high frequency noise bursts, sudden baseline movement, and cycle variability; and

rejecting, by the processor, the received set of biophysical signal data based on the comparison.

18. The method of claim 17, wherein comparing the received set of biophysical signal data to powerline interference comprises determining a powerline coefficient of the set of biophysical signal data, and wherein rejecting the received set of biophysical signal data comprises rejecting the set of biophysical signal data when the powerline coefficient exceeds a predetermined threshold.

19. The method of claim 16 or 17, wherein comparing the received biophysical signal data set to high frequency noise comprises determining a high frequency noise score for the biophysical signal data set, and wherein rejecting the received biophysical signal data set comprises rejecting the biophysical signal data set when the high frequency noise score exceeds a predetermined threshold.

20. The method of any one of claims 16-19, wherein comparing the received biophysical signal dataset to a high frequency noise burst comprises testing a one-second window using a high frequency time series and comparing the one-second window to a threshold to determine a high frequency noise burst for the biophysical signal dataset, and wherein rejecting the received biophysical signal dataset comprises rejecting the biophysical signal dataset when the one-second energy is greater than the threshold.

21. The method of any one of claims 16-20, wherein comparing the received biophysical signal data set to the sudden baseline movement comprises determining the sudden movement of the baseline when the baseline in a predetermined time window of the signal changes more than a predetermined amount relative to a previous window, and wherein rejecting the received biophysical signal data set comprises rejecting the biophysical signal data set when the sudden movement in the baseline is determined.

22. The method of any one of claims 16-21, wherein comparing the received biophysical signal dataset to period variability comprises determining period variability noise, and wherein rejecting the received biophysical signal dataset comprises rejecting the biophysical signal dataset when the period variability noise exceeds a predetermined threshold.

23. The method of any one of claims 16-22, wherein the comparing comprises determining that asynchronous noise present in the acquired biophysical signal dataset has a value or energy that exceeds a predefined threshold.

24. The method of any one of claims 1-23, further comprising:

generating, by the processor, a notification of a failure to acquire the set of biophysical signal data.

25. The method of any one of claims 1-24, wherein the notification prompts a subsequent acquisition of a biophysical signal dataset to be performed.

26. The method of any one of claims 1-25, further comprising:

causing, by the processor, the received biophysical signal data set to be transmitted to an external analysis system via a network, wherein the analysis system is configured to analyze the received biophysical signal data to determine the presence or extent of a pathological or clinical condition.

27. A system, comprising:

one or more processors; and

a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors causes the one or more processors to perform the method of any one of claims 1-26.

28. A non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions by one or more processors causes the one or more processors to perform the method of any one of claims 1-26.

Technical Field

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

Background

Ischemic heart disease (also known as cardiac ischemia or myocardial ischemia) is a disease or group of diseases characterized by a reduction in the blood supply to the heart muscle, usually caused by Coronary Artery Disease (CAD). CAD typically occurs when atherosclerosis (hardening or stiffening of the inner wall and the accumulation of plaque therein, often accompanied by abnormal inflammation) occurs in the inner wall of the coronary arteries supplying the myocardium or myocardium. Over time, CAD can also weaken the myocardium and lead to, for example, angina, myocardial infarction (sudden cardiac arrest), heart failure, and cardiac arrhythmias. An arrhythmia is an abnormal heart rhythm that may include any change in the normal sequence of electrical conduction through the heart, which in some cases may lead to cardiac arrest.

The evaluation of CAD can be complex and use many techniques and tools to assess the presence and severity of a disorder. In the case of electrocardiography (i.e., the field of information cardiology in which the electrical activity of the heart is analyzed to obtain information about its structure and function), severe ischemic heart disease can alter the narrowing or occlusion of the ventricular conduction properties of the myocardium in the perfusion bed downstream of the coronary arteries. This pathology can manifest itself in different severity levels at different locations of the heart, which makes accurate diagnosis challenging. Furthermore, the conductive properties of the myocardium may vary from person to person, and other factors, such as variability of measurements related to placement of the measurement probe and parasitic losses associated with such probes and their associated components, may also affect the biophysical signals captured during electrophysiological examination of the heart. Furthermore, when the conductive properties of the myocardium are captured as relatively long cardiac phase gradient signals, they may exhibit complex non-linear variability that conventional modeling techniques cannot effectively capture.

The signal quality of the acquired biophysical signals (whether cardiac, neural, or other biophysical signals) may be affected by noise. Such noise, which may come from a variety of sources, may affect the assessment of a patient, including the clinical assessment of the patient's biological system or systems associated with such signals, as well as any associated disorders or pathologies. In the case of cardiac signals, such noise may affect some or all of the acquired signals, reducing the effectiveness of the assessment of CAD, arrhythmia, pulmonary hypertension, heart failure (e.g., any condition or symptom associated with, related to, or affected (directly or indirectly) by the cardiac signal), thereby exposing the patient to the risk of incorrect assessment and/or diagnosis.

Furthermore, if problems such as poor signal quality do have an adverse effect, some or all of the acquired signals may have to be ignored and new signals acquired from the patient. In some cases, this may result in having to retrieve the assessment results, which is inconvenient for the patient, as the patient will have to return to a doctor's office, hospital, or other clinical environment, and increase the cost of the healthcare system.

Disclosure of Invention

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

As used herein, the term "cardiac signal" refers to one or more signals associated with the structure, function and/or activity of the cardiovascular system, including aspects of electrical/electrochemical conduction of the signal, e.g., causing contraction of the myocardium. In some embodiments, the cardiac signal may include electrocardiogram signals, such as those acquired via an Electrocardiogram (ECG) or other means.

As used herein, the term "neurological signal" refers to one or more signals associated with the structure, function and/or activity of the central and peripheral nervous systems, including the brain, spinal cord, nerves and their associated neurons and other structures, and the like, and includes the electrical/electrochemical transduction aspects of the signal. In some embodiments, the neural signals may include electroencephalographic signals, such as those acquired through electroencephalography (EEG) or otherwise.

As used herein, the term "biophysical signal" is not limited to cardiac signals, neurological signals, or photoplethysmographic signals, but encompasses any physiological signal from which information may be obtained. Without limitation by example, biophysical signals may be classified into types or categories that may include: such as electrical signals (e.g., certain heart and nervous system related signals that may be observed, identified and/or quantified by techniques such as measuring voltage/potential, impedance, resistivity, conductivity, current, etc., in various domains, such as the time and/or frequency domains), magnetic signals, electromagnetic signals, optical signals (e.g., signals that may be observed, identified and/or quantified by techniques such as reflection, interference, spectroscopy, absorbance, transmittance, visual observation, photoplethysmographic waves, etc.), acoustic signals, chemical signals, mechanical signals (e.g., signals related to fluid flow, pressure, motion, vibration, displacement, strain), thermal signals, and electrochemical signals (e.g., signals that may be associated with the presence of certain analytes such as glucose). In some cases, biophysical signals may be described in the context of the following physiological systems: for example, respiratory system, circulatory system (cardiovascular, pulmonary), neurological, lymphatic, endocrine, digestive, excretory, muscle, skeletal, renal/urinary/excretory, immune, epidermal/exocrine, and reproductive systems), organ system (e.g., signals that may be unique to the heart and lungs, as they work together), or tissue (e.g., muscle, fat, nerve, connective tissue, bone), cells, organelles, molecules (e.g., water, proteins, fats, carbohydrates, gases, free radicals, inorganic ions, minerals, acids, and other compounds, elements, and sub-atomic components thereof). Unless otherwise indicated, the term "biophysical signal acquisition" generally refers to any passive or active manner of acquiring a biophysical signal from a physiological system (e.g., a mammalian or non-mammalian organism). Passive biophysical signal acquisition generally refers to the observation of natural or induced electrical, magnetic, optical, and/or acoustic emissivity of body tissue. Non-limiting examples of passive and active biophysical signal acquisition means include, for example, natural radiation that observes body tissue in voltage/potential, current, magnetic, acoustic, light, and other non-active ways, and in some cases induces such radiation. Non-limiting examples of passive and active biophysical signal acquisition means include active means such as ultrasound, radio waves, microwaves, infrared and/or visible light (e.g., for pulse oximetry or photoplethysmography), visible light, ultraviolet light, etc., to interrogate body tissue (not involving ionizing energy or radiation (e.g., X-rays)). Active biophysical signal acquisition may also involve the transmission of ionizing energy or radiation (e.g., X-rays) (also referred to as "ionizing biophysical signals") to body tissue. Passive and active biophysical signal acquisition means may be performed in conjunction with invasive procedures (e.g., by surgery or invasive radiological intervention protocols) or may be performed non-invasively (e.g., by imaging).

Although the present disclosure is directed to quantification of biophysical signal quality that is beneficial in the diagnosis and treatment of heart-related pathologies and disorders and/or neurological-related pathologies and disorders, such quantification may be applied to the diagnosis and treatment (including surgery, minimally invasive and/or drug treatment) of any pathology or disorder in which the biophysical signal is related to any relevant system of the living body. An example of a cardiac context is the diagnosis of CAD and its treatment by various therapies (alone or in combination), such as placing stents in coronary arteries, performing atherectomy, angioplasty, prescription of medications and/or exercise prescriptions, nutritional and other lifestyle changes, and the like. Other heart-related pathologies or conditions that may be diagnosed include, for example, cardiac arrhythmias, congestive heart failure, valve failure, pulmonary hypertension (e.g., pulmonary hypertension due to left heart disease, pulmonary hypertension due to pulmonary disease, pulmonary hypertension due to chronic thrombosis, and pulmonary hypertension due to other diseases (e.g., blood or other diseases)), and other heart-related pathologies, conditions, and/or diseases. Non-limiting examples of diagnosable nerve-related diseases, pathologies or conditions include, for example, epilepsy, schizophrenia, Parkinson's disease, Alzheimer's disease (and all other forms of dementia), autism spectrum (including Alzheimer's syndrome), attention deficit hyperactivity disorder, Huntington's disease, muscular dystrophy, depression, bipolar disorder, brain/spinal cord tumors (malignant and benign), movement disorders, cognitive disorders, language disorders, various psychoses, brain/spinal cord/nerve injury, chronic traumatic encephalopathy, cluster headache, migraine, neuropathy (various forms including peripheral neuropathy), phantom limb/pain, chronic fatigue syndrome, acute and/or chronic pain (including back pain, back surgery failure syndrome, etc.), motor dysfunction, anxiety disorders, diseases caused by infection or extrinsic factors (such as Lyme disease, Huntington's disease, muscular dystrophy), Encephalitis, rabies), narcolepsy and other sleep disorders, post-traumatic stress disorder, neurological disorders/effects associated with stroke, aneurysm, hemorrhagic damage, and the like, tinnitus and other hearing related diseases/disorders, and vision related diseases/disorders.

Skeletal muscle related signals (e.g., as characterized in Electromyography (EMG)) are typically characterized as "in-band noise" with respect to cardiac signals, neurological signals, etc. -i.e., it often occurs within the same or similar frequency ranges in the acquired biophysical signals of interest. For example, for cardiac signals, the dominant frequency component of the generated signal is typically between about 0.5Hz to about 80 Hz. For neural signals, such as brain signals, the frequency component is typically between about 0.1Hz to about 50 Hz. Furthermore, the skeletal muscle related signals may also have the same or similar amplitude as typical heart-based and nervous system-based waveforms, etc., depending on the degree of contamination. Indeed, the similarity of skeletal muscle related signals to cardiac signals, neural signals, and other biophysical signals, etc., may have a significant impact on the analysis of the biophysical signals of interest. Therefore, quantifying the signal quality of the measured biophysical signal is crucial for e.g. quality assessment of the acquired biophysical signal of interest and avoiding the use of contaminated acquired signals in subsequent analyses, which provides information useful in subsequent analyses to enable compensation for contamination etc.

The methods and systems described in various embodiments herein are not so limited and may be used in any context of another physiological system or system, organ, tissue, cell, etc. of a living subject. By way of example only, two biophysical signal types that may be used in a cardiovascular environment include cardiac signals that may be acquired by conventional electrocardiogram (ECG/EKG) devices, bipolar broadband biopotential (cardiac) signals that may be acquired from other devices, such as those described herein, and signals that may be acquired by various volume description techniques, such as photoplethysmography.

In the context of the present disclosure, techniques are described for acquiring and analyzing biophysical signals, in particular for diagnosing the presence, absence, location (where applicable) and/or severity of certain disease states or conditions in, associated with or affecting the cardiovascular (or cardiac) system, including, for example, Pulmonary Hypertension (PH), Coronary Artery Disease (CAD), and heart failure (e.g., left or right heart failure).

Pulmonary hypertension, heart failure, and coronary artery disease are three diseases/conditions associated with the cardiovascular or cardiac system. Pulmonary arterial hypertension (PH) generally refers to hypertension in the arteries of the lungs, which may include a range of conditions. PH often has a complex and multifactorial etiology and has occult clinical episodes of varying severity. PH can develop complications, such as right heart failure, and in many cases is fatal. The World Health Organization (WHO) classifies PH into five groups or 5 types. The first PH group of WHO classification is Pulmonary Arterial Hypertension (PAH). PAH is a chronic disease that is currently incurable, and in particular, it causes the pulmonary artery wall to tighten and stiffen. PAH requires at least cardiac catheterization for diagnosis. PAH is characterized by pulmonary arterial vasculopathy, defined in cardiac catheterization as a mean pulmonary arterial pressure of 25mmHg or higher. One form of pulmonary hypertension is known as idiopathic pulmonary hypertension-PAH that occurs without a clear cause. In addition, a subset of PAHs includes hereditary PAHs, drug and toxin induced PAHs, as well as PAHs associated with other systemic diseases (e.g., connective tissue disease, HIV infection, portal hypertension, and congenital heart disease). PAH includes all causes that result in narrowing of the pulmonary vascular structure. In PAH, the gradual narrowing of the pulmonary artery bed is due to an imbalance of vasoactive mediators (including prostacyclin, nitric oxide and endothelin-1). This can lead to increased right ventricular afterload, right heart failure and premature death. The second PH group of the WHO classification is pulmonary hypertension due to left heart disease. This group of diseases is often characterized by problems on the left side of the heart. Over time, these problems can cause changes in the pulmonary arteries. Specific subgroups include left ventricular systolic dysfunction, left ventricular diastolic dysfunction, valvular disease, and ultimately congenital cardiomyopathy and obstruction due to non-valvular disease. Treatment of the second PH group often focuses on potential problems (e.g., heart valve replacement surgery, various medications, etc.). The third PH group of the WHO classification is large and diverse and is often associated with pulmonary disease or hypoxia. 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 is classified by WHO as a chronic thromboembolic pulmonary hypertension whose blood clots enter or are caused upon lung formation, blocking the flow of blood through the pulmonary arteries. The fifth PH group is classified by the WHO as including subgroups of rare diseases that cause PH, such as hematological diseases, systemic diseases (e.g., sarcoidosis which affects the lungs), metabolic disorders and other diseases. The mechanism of PH in this fifth group is poorly understood.

All forms of PH are difficult to diagnose in routine physical examination, as the most common symptoms of PH (shortness of breath, fatigue, chest pain, edema, palpitations, dizziness) are associated with many other conditions. Blood tests, chest X-ray tests, electrocardiography and echocardiography, pulmonary function tests, exercise tolerance tests, and nuclear scanning are all widely used to help physicians diagnose certain forms of PH. As mentioned above, the "gold standard" for diagnosing PH, and in particular PAH, is cardiac catheterization of the right side of the heart by directly measuring the pressure in the pulmonary artery. If the subject is suspected of having PAH, one of a plurality of surveys may be conducted to confirm the condition, such as electrocardiogram, chest radiograph, and lung function tests, among others. It is commonly seen that electrocardiograms show right heart strain and chest radiographs show evidence of significant pulmonary artery or cardiac hypertrophy. However, a normal electrocardiogram and chest radiograph cannot exclude the diagnosis of PAH. Further testing may be required to confirm diagnosis and to determine etiology and severity. For example, blood tests, exercise tests, and overnight oximetry tests may be performed. In addition, imaging tests may also be performed. Examples of imaging tests include isotope perfusion lung scans, high resolution computed tomography scans, computed tomography lung angiography, and magnetic resonance lung angiography. If these (and possibly other) non-invasive examinations support a diagnosis of PAH, right heart catheterization is often required to confirm the diagnosis by directly measuring lung pressure. It also allows measurement of cardiac output and estimation of left atrial pressure using pulmonary artery wedge pressure. Although non-invasive techniques exist to determine whether PAH is present in a subject, these techniques do not reliably confirm the diagnosis of PAH unless invasive right heart catheterization is performed. Aspects and embodiments of methods and systems for assessing PH are disclosed in commonly-owned U.S. patent application No. 16/429,593, which is incorporated herein by reference in its entirety.

Heart failure affects nearly 600 million people in the united states alone, with over 870,000 people diagnosed with heart failure 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 needs of the body, either because the heart muscle is weakened or stiff, or because there is a defect that prevents normal circulation. This can result in, for example, blood and fluid regurgitation to the lungs, edema, fatigue, dizziness, fainting, a rapid and/or irregular heartbeat, dry cough, nausea and shortness of breath. Common causes of heart failure are Coronary Artery Disease (CAD), hypertension, cardiomyopathy, arrhythmia, kidney disease, heart defects, obesity, smoking, and diabetes. Diastolic Heart Failure (DHF), left or left sided heart failure/disease (also referred to as left ventricular heart failure), right or right sided heart failure/disease (also referred to as right ventricular heart failure) and Systolic Heart Failure (SHF) are common types of heart failure.

Left-sided heart failure is further divided into two main types: systolic failure (or heart failure with reduced ejection fraction or reduced left ventricular function) and diastolic failure/dysfunction (or heart failure with preserved ejection fraction or preserved left ventricular function). Procedures and techniques commonly used to determine whether a patient has left-sided heart failure include cardiac catheterization, X-ray, echocardiogram, Electrocardiogram (EKG), electrophysiology studies, radionuclide imaging, and various treadmill tests, including tests that measure peak VO 2. Ejection Fraction (EF) is a measure expressed as a percentage of the amount of blood pumped out by the ventricle on each contraction (left ventricle in the case of left-sided heart failure), most commonly obtained via echocardiography in a non-invasive manner. The normal Left Ventricular Ejection Fraction (LVEF) ranges from about 55% to about 70%.

When systolic failure occurs, the left ventricle cannot contract vigorously to maintain normal systemic blood circulation, thereby depriving the body of the normal blood supply. As the left ventricular pump becomes more difficult to compensate, it becomes weaker and thinner. As a result, blood flows back into the organ, causing fluid accumulation in the lungs and/or swelling in other parts of the body. Echocardiography, magnetic resonance imaging, and nuclear medicine scanning (e.g., multi-gated acquisition) are techniques for non-invasively measuring the Ejection Fraction (EF), expressed as a percentage of the left ventricular pump volume relative to its fill volume, to help in diagnosing systolic failure. In particular, Left Ventricular Ejection Fraction (LVEF) values below 55% indicate that the pumping capacity of the heart is lower than normal, and in severe cases can be measured to be below about 35%. In general, when these LVEF values are lower than normal, diagnosis of systolic failure can be made or facilitated.

When diastolic heart failure occurs, the left ventricle becomes stiff or thicker, losing its ability to relax normally, which in turn means that the left lower chamber of the heart is not properly engorged with blood. This reduces the amount of blood pumped out to the body. Over time, this can lead to blood accumulation in the left atrium and then in the lungs, leading to fluid engorgement and heart failure symptoms. In this case, the LVEF value tends to remain within the normal range. Thus, other tests (e.g., invasive catheterization) may be used to measure Left Ventricular End Diastolic Pressure (LVEDP) to help diagnose diastolic heart failure as well as other forms of EF-preserved heart failure. Typically, LVEDP is measured directly by placing a catheter in the left ventricle or indirectly by placing a catheter in the pulmonary artery to measure pulmonary capillary wedge pressure. By their nature, such catheterization techniques increase the risk of infection and other complications for the patient and are often expensive. Accordingly, non-invasive methods and systems for determining or estimating LVEDP are desirable in diagnosing the presence or absence and/or severity of diastolic heart failure, as well as myriad other forms of heart failure with retained EF. Furthermore, non-invasive methods and systems (not necessarily including the determination or estimation of abnormal LVEDP) for diagnosing the presence or absence and/or severity of diastolic heart failure, as well as myriad other forms of heart failure with retained EF, are desirable. Embodiments of the present disclosure address all of these needs.

Right-sided heart failure typically occurs as a result of left-sided heart failure, when the weak and/or stiff left ventricle loses its ability to effectively pump blood to other parts of the body. As a result, fluid is forced back through the lungs, weakening the right side of the heart, resulting in right-side heart failure. This reflux backs up in the veins, causing fluid in the legs, ankles, gastrointestinal tract and liver to expand. In other cases, certain lung diseases (e.g., chronic obstructive pulmonary disease and pulmonary fibrosis) can lead to right-sided heart failure, even if left-sided cardiac function is normal. Procedures and techniques commonly used to determine whether a patient has left-sided heart failure include blood tests, cardiac CT scans, cardiac catheterization, X-ray, coronary angiography, echocardiography, Electrocardiogram (EKG), myocardial biopsy, lung function studies, and various forms of stress testing, such as treadmill testing.

Pulmonary hypertension is closely associated with heart failure. As mentioned above, PAH (first WHO PH grouping) can lead to increased right ventricular afterload, right heart failure, and premature death. PH caused by left heart failure (second WHO PH group) is considered to be the most common cause of PH.

Ischemic heart disease (also known as cardiac ischemia or myocardial ischemia) and related conditions or pathologies may also be estimated or diagnosed using the techniques disclosed herein. Ischemic heart disease is a disease or group of diseases characterized by a reduced blood supply to the heart muscle, usually caused by Coronary Artery Disease (CAD). CAD is closely related to heart failure, and is the most common cause thereof. CAD typically occurs when atherosclerosis (hardening or stiffening of the inner wall and the accumulation of plaque therein, often accompanied by abnormal inflammation) occurs in the inner wall of the coronary arteries that supply the heart muscle or muscles. Over time, CAD can also weaken heart muscle and cause, for example, angina, myocardial infarction (sudden cardiac arrest), heart failure, and cardiac arrhythmias. An arrhythmia is an abnormal heart rhythm that may include any change in the normal sequence of electrical conduction through the heart, which 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 use a number of invasive techniques and tools to assess the presence and severity of such conditions. Furthermore, the commonality between the symptoms of these diseases and/or disorders and the fundamental link between the respiratory system and the cardiovascular system, as they work together to supply oxygen to the cells and tissues of the body, suggests a complex physiological interrelationship that can be used to improve the detection and ultimate treatment of such diseases and/or disorders. In such cases, traditional methods of assessing these biophysical signals still present significant challenges in providing healthcare providers with tools for accurately detecting/diagnosing the presence or absence of such diseases and disorders.

For example, in electrocardiography, the field of cardiology in which the electrical activity of the heart is analyzed to obtain information about its structure and function, it has been observed that significant ischemic heart disease can alter the ventricular conduction properties of the perfused bed myocardium downstream of the coronary arteries, causing it to narrow or occlude, and pathology can manifest itself at different locations and at different stages of severity in the heart, making accurate diagnosis challenging. Furthermore, when the conduction properties of the myocardium are captured as relatively long cardiac phase gradient signals, they may exhibit complex non-linear variability that conventional modeling techniques cannot effectively capture.

In one aspect, a method of acquiring a biophysical signal dataset for clinical analysis (e.g., as part of a machine learning dataset or for clinical diagnosis) is disclosed, the method comprising: obtaining, by a processor, a biophysical signal dataset of a subject, or a portion thereof, for measurement (e.g., measurement of a heart, brain, lung, etc. of the subject), wherein the biophysical signal dataset or portion thereof is acquired via one or more surface probes of a non-invasive measurement system (e.g., placed on the chest of the subject), through one or more corresponding channels, and within an acquisition time period suitable for subsequent evaluation (e.g., greater than about 120 seconds, such as approximately about 210 seconds), wherein the acquisition time period is predefined, dynamically determined, or set by a user; determining, by the processor (e.g., of a non-invasive measurement system), one or more signal quality parameters of the obtained biophysical signal dataset, wherein at least one of the one or more signal quality parameters is selected from the group consisting of: a power line interference parameter related to power line noise pollution, a high frequency noise parameter related to high frequency noise pollution, a noise burst parameter related to high frequency noise burst pollution, a sudden movement parameter related to sudden movement pollution, and an asynchronous noise parameter related to skeletal muscle pollution or heart cycle variability; and when the one or more signal quality parameters fail the noise quality assessment performed on the one or more signal quality parameters, rejecting, by the processor, the obtained biophysical signal dataset or the portion thereof evaluated (e.g., where rejecting causes the processor to output a visual indicator of a failed evaluation at the non-invasive measurement system, an audio indicator of a failed evaluation at the non-invasive measurement system, and a report of a failed evaluation at the non-invasive measurement system, where the outputting is concurrent or nearly concurrent with the measuring) (e.g., where the rejecting facilitates acquisition of a second biophysical signal dataset or a portion thereof of the subject immediately after acquisition of the biophysical signal) (e.g., where the evaluating of non-rejection or acceptance of the obtained biophysical signal dataset causes the processor to transmit the obtained biophysical signal dataset over a network for remote clinical analysis).

In some embodiments, the method further comprises: outputting, at the non-invasive measurement system, one or more of a visual indicator, an audio indicator, a vibration indicator, and a report that failed to evaluate, wherein the outputting is concurrent or nearly concurrent with the measuring (e.g., facilitating acquisition of a second biophysical signal dataset, or a portion thereof, of the subject immediately after acquisition of the biophysical signal).

In some embodiments, the method further comprises: after a non-repudiation or an acceptance evaluation of the obtained biophysical signal dataset, transmitting, by the processor, the obtained biophysical signal dataset over a network for remote clinical analysis.

In some embodiments, the method further comprises: acquiring, by one or more acquisition circuits of the measurement system, voltage gradient signals on the one or more channels, wherein the voltage gradient signals are acquired at a frequency greater than about 1 kHz; and generating, by the one or more acquisition circuits, the obtained biophysical data set from the acquired voltage gradient signals.

In some embodiments, the method further comprises: placing at least a first surface probe at a first axis of the subject passing left to right through the subject's body; placing at least a second surface probe at a second axis of the subject that passes through the subject's body from top to bottom; and placing at least a third surface probe at a third axis passing through the subject's body from front to back, wherein the first axis, the second axis, and the third axis are mutually orthogonal axes.

In some embodiments, the obtained set of biophysical signal data, or the evaluated portion thereof, is rejected when the power line interference parameter of any of the one or more channels does not comply with a power line interference condition (e.g., exceeds a power line interference threshold).

In some embodiments, the obtained biophysical signal dataset or the assessed portion thereof is rejected when the high frequency noise parameter associated with high frequency noise contamination of any of the one or more channels does not comply with a high frequency noise condition (e.g., when the high frequency noise parameter exceeds a predetermined high frequency noise threshold).

In some embodiments, the obtained biophysical signal dataset or the evaluated portion thereof is rejected when the noise burst parameter associated with high frequency noise burst pollution for any of the one or more channels does not comply with the noise condition (e.g., a high frequency time series is used to test a one second window and compare the one second window to a median high frequency energy, and reject the biophysical signal dataset when the one second energy is greater than twice the median).

In some embodiments, the obtained biophysical signal dataset, or the evaluated portion thereof, is rejected when the sudden movement parameter associated with sudden movement contamination for any of the one or more channels does not comply with the sudden movement condition (e.g., when the baseline changes relative to the previous window in a one second window of the signal by more than 25% of the ventricular depolarization amplitude of the channel).

In some embodiments, the obtained biophysical signal dataset, or the assessed portion thereof, is rejected when an asynchronous noise parameter of any of the one or more channels does not comply with an asynchronous noise condition (e.g., when a cardiac cycle variability exceeds a predetermined threshold), wherein the asynchronous noise parameter may include skeletal muscle contamination or cardiac cycle variability.

In some embodiments, the power line coefficients are determined by: performing a fourier transform (e.g., a fast fourier transform) on the obtained biophysical signal dataset or portion thereof by the processor; and determining, by the processor, a maximum power line energy for a plurality of frequency ranges (e.g., between about 50Hz, such as between about 48Hz and about 52 Hz; between about 60Hz, such as between about 58Hz and about 62 Hz; between about 150Hz, such as between about 145Hz and about 155 Hz; between about 180Hz, such as between about 175Hz and about 185 Hz; between about 300Hz, such as between about 295Hz and about 305 Hz).

In some embodiments, the assessment is a gated phase for subsequent analysis of the subject for coronary artery disease, pulmonary hypertension, or other pathology or disease state.

In some embodiments, the received biophysical signal dataset comprises a cardiac signal dataset.

In some embodiments, the biophysical signal dataset is generated in near real-time as the biophysical signals are acquired.

In some embodiments, the biophysical signal is acquired from a sensor in a smart device or a handheld medical diagnostic apparatus.

In some embodiments, the biophysical signal dataset comprises wideband cardiac phase gradient cardiac signal data derived from biopotential signals simultaneously captured from a plurality of surface electrodes placed on a body surface proximate to the subject's heart.

In another aspect, a method of rejecting an acquired biophysical signal is disclosed, the method comprising: receiving, by a processor, a biophysical signal dataset of a subject; comparing, by the processor, the received biophysical signal dataset to at least one of: power line interference, high frequency noise bursts, sudden baseline movement, and cycle variability; and rejecting, by the processor, the received set of biophysical signal data based on the comparison.

In some embodiments, comparing the received set of biophysical signal data to powerline interference comprises determining a powerline coefficient of the set of biophysical signal data, and wherein rejecting the received set of biophysical signal data comprises rejecting the set of biophysical signal data when the powerline coefficient exceeds a predetermined threshold.

In some embodiments, comparing the received biophysical signal data set to high frequency noise comprises determining a high frequency noise score for the biophysical signal data set, and wherein rejecting the received biophysical signal data set comprises rejecting the biophysical signal data set when the high frequency noise score exceeds a predetermined threshold.

In some embodiments, comparing the received biophysical signal dataset to the high frequency noise burst comprises determining the high frequency noise burst of the biophysical signal dataset using a high frequency time series testing a one second window and comparing the one second window to a threshold, and wherein rejecting the received biophysical signal dataset comprises rejecting the received biophysical signal dataset when the one second energy is greater than the threshold.

In some embodiments, comparing the received biophysical signal data set to the sudden baseline movement comprises determining the sudden movement of the baseline when the baseline in a predetermined time window of the signal changes more than a predetermined amount relative to a previous window, and wherein rejecting the received biophysical signal data set comprises rejecting the received biophysical signal data set when the sudden movement in the baseline is determined.

In some embodiments, comparing the received set of biophysical signal data to the variability of cycles comprises determining a variability of cycles noise, and wherein rejecting the received set of biophysical signal data comprises rejecting the set of biophysical signal data when the variability of cycles noise exceeds a predetermined threshold.

In some embodiments, the comparing comprises determining whether asynchronous noise present in the acquired biophysical signal dataset has a value or energy that exceeds a predefined threshold.

In some embodiments, the method further comprises: generating, by the processor, a notification of a failure to acquire the set of biophysical signal data.

In some embodiments, the notification prompts a subsequent acquisition of the set of biophysical signal data to be performed.

In some embodiments, the method further comprises: causing, by the processor, the received biophysical signal data set to be transmitted via a network to an external analysis system, wherein the analysis system is configured to analyze the received biophysical signal data for the presence or extent of a pathological or clinical condition.

In some embodiments, a system is disclosed, comprising: one or more processors; and a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors causes the one or more processors to perform a method as described above.

In some embodiments, a non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions by one or more processors causes the one or more processors to perform the method as described above is disclosed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and together with the description, serve to explain the principles of the methods and systems incorporated herein. The embodiments are better understood from the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures:

fig. 1A is a diagram of an exemplary system configured to quantify and remove asynchronous noise and artifact contamination to more accurately assess complex non-linear variability in a quasi-periodic system (e.g., a biological system with biophysical signals), according to an example embodiment.

Fig. 1B is a diagram of an exemplary system configured to reject acquired biophysical signals based on asynchronous noise and quantification of artifact contamination, according to another example embodiment.

FIG. 2 is a diagram of an illustrative evaluation system in accordance with an exemplary embodiment.

Fig. 3 is a diagram illustrating a signal quality assessment system according to an exemplary embodiment.

Fig. 4 is an operational flow diagram of an implementation of a method of assessing signal quality according to another exemplary embodiment.

Fig. 5 is an operational flow diagram of an implementation of a method of evaluating power line interference according to another example embodiment.

Fig. 6 is a graph illustrating observable characteristics of maximum power line interference (e.g., of the power line interference assessment operation of fig. 5) in accordance with another example embodiment.

Fig. 7 is a graph illustrating observable characteristics of power line interference at a threshold (e.g., of the power line interference assessment operation of fig. 5), in accordance with an example embodiment.

FIG. 8 is an operational flow diagram of an implementation of a method of evaluating high frequency noise according to an exemplary embodiment.

FIG. 9 is a graph illustrating observable characteristics of maximum high frequency noise (e.g., of the high frequency noise estimation operation of FIG. 8), in accordance with an example embodiment.

FIG. 10 is a graph illustrating observable characteristics of high frequency noise at a threshold (e.g., of the high frequency noise assessment operation of FIG. 8), according to an example embodiment.

Fig. 11 is an operational flow diagram of an implementation of a method of evaluating high frequency noise bursts in accordance with an exemplary embodiment.

FIG. 12 is a graph illustrating observable characteristics of a maximum high frequency noise burst (e.g., of the high frequency noise burst evaluation operation of FIG. 11), in accordance with an example embodiment.

Fig. 13 is a graph illustrating observable characteristics of a next highest frequency noise burst (e.g., of the high frequency noise burst evaluation operation of fig. 11), in accordance with an example embodiment.

FIG. 14 is an operational flow diagram of an implementation of a method of evaluating sudden baseline movement according to an exemplary embodiment.

FIG. 15 is a graph illustrating observable characteristics of maximum abrupt movement (e.g., of the abrupt baseline movement assessment operation of FIG. 14), according to an example embodiment.

Fig. 16 is a graph illustrating observable characteristics of a 50% sudden movement (e.g., of the sudden baseline motion assessment operation of fig. 14), in accordance with an example embodiment.

FIG. 17 is an operational flow diagram of an implementation of a method of evaluating cycle variability according to an exemplary embodiment.

Fig. 18 is a graph illustrating observable characteristics of the highest cycle variability noise (e.g., of the cycle variability evaluation operation of fig. 17), according to an example embodiment.

Fig. 19 is a graph illustrating observable characteristics of lower variability of periodicity noise (e.g., of the periodicity variability evaluation operation of fig. 17), according to an example embodiment.

Fig. 20 is an operational flow diagram of an implementation of a method of assessing signal quality in accordance with an exemplary embodiment.

Fig. 21A and 21B show an architecture and data flow of an exemplary signal quality assessment component according to an exemplary embodiment.

FIG. 22 illustrates an exemplary computing environment in which exemplary embodiments and aspects may be implemented, according to an exemplary embodiment.

Detailed Description

Each feature described herein, and each combination of two or more such features, is included within the scope of the present invention provided that the features included in such a combination are not mutually inconsistent.

As further described herein, the signal quality of the acquired signals is evaluated in real time, and if the acquired signals are corrupted by noise, a notification is generated and provided to the attending technician. In one or more implementations, evaluating includes: (1) detecting power line interference; (2) detection of sudden movements; (3) detecting a noise burst; (4) confirmation of a minimum signal-to-noise ratio (SNR); and/or (5) detection of asynchronous noise (e.g., Electromyography (EMG) noise).

As further described herein, acquiring signals and determining in real-time whether the acquisition is problematic (e.g., the acquired signals are immediately processed to determine whether the acquired signals are acceptable or unacceptable, whether the quality of the acquired signals is sufficient for subsequent evaluation, etc.); if there is a problem, an output is provided to indicate that signal acquisition needs to be performed again (i.e., if the acquired signal is not acceptable, the acquired signal is rejected).

Fig. 1A is a diagram of an example system 100, according to an example embodiment, the example system 100 configured to quantify and remove asynchronous noise (e.g., artifact noise pollution associated with skeletal muscle), and use such quantification to more accurately assess complex non-linear variability in a quasi-periodic system. As used herein, the term "remove" and other similar terms refer to any meaningful reduction of all or part of the noise pollution that improves or is beneficial for subsequent analysis.

In fig. 1A, a measurement system 102 is a non-invasive embodiment (shown as a "measurement system (biophysics)" 102) that acquires a plurality of biophysical signals 104 from a subject 106 via any number of measurement probes 114 (shown in the system 100 in fig. 1 as including 6 such probes 114a, 114b, 114c, 114d, 114e, and 114f) to produce a biophysical signal dataset 108 that can be used 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 estimated physiological characteristics of the physiological system under study and/or the presence or absence of a disease. In other embodiments, there is no clinical output, but rather information is output that clinicians may use to provide their own clinical assessment of information about the patient whose signals are being assessed.

In some embodiments, and as shown in fig. 1A, the measurement system 102 is configured to remove asynchronous noise contamination from the amplified and digitized biophysical signal data set 117 that has been processed/conditioned by the front-end amplification and digitization operation 116 (e.g., via operation 118). The noise pollution removal operation 118 is based on the quantification of asynchronous noise that may be present in the data set 117. In some embodiments where asynchronous noise is removed, once a representative periodic data set is established (e.g., from several samples of the acquired biophysical signal data set 108), operation 118 may be performed in near real-time, e.g., via a processor and corresponding instructions or via digital circuitry (e.g., a CPLD, a microcontroller, etc.). The acquired biophysical signal dataset 108 refers to any dataset (e.g., 117, 108) generated by the measurement system 102 or generated within the measurement system 102 after the front-end amplification and digitization operations 116. In some embodiments, hundreds of samples may be used to build a representative periodic data set. In other embodiments, thousands of samples may be used to build a representative periodic data set. In some embodiments, the quantization of asynchronous noise is performed in hardware circuitry integrated into and operating with front-end amplification and digitization operation 116.

In some embodiments, the measurement system 102 is configured to acquire, via the biopotential sensing circuit, a biophysical signal that may be based on a biopotential of the body as a biopotential biophysical signal. In a cardiac and/or electrocardiographic environment, the measurement system 102 is configured to capture a cardiac-related biopotential signal or electrophysiological signal of a living subject (e.g., a human) as a biopotential cardiac signal data set. In some embodiments, measurement system 102 is configured to acquire a broadband cardiac phase gradient signal as a biopotential signal or other signal type (e.g., a current signal, an impedance signal, a magnetic signal, an optical signal, an ultrasound or acoustic signal, etc.). The term "wideband" with respect to the acquired signals and their corresponding data sets refers to signals having a frequency range significantly greater than the nyquist sampling rate of the highest dominant frequency of the physiological system of interest. For cardiac signals typically having a dominant frequency component between about 0.5Hz and about 80Hz, the wideband cardiac phase gradient signal or wideband cardiac biophysical signal includes cardiac frequency information for a frequency selected from the group consisting of: between about 0.1Hz and about 1KHz, between about 0.1Hz and about 2KHz, between about 0.1Hz and about 3KHz, between about 0.1Hz and about 4KHz, between about 0.1Hz and about 5KHz, between about 0.1Hz and about 6KHz, between about 0.1Hz and about 7KHz, between about 0.1Hz and about 8KHz, between about 0.1Hz and about 9KHz, between about 0.1Hz and about 10KHz, and between about 0.1Hz and greater than 10KHz (e.g., 0.1Hz to 50KHz or 0.1Hz to 500 KHz). In addition to capturing the primary frequency components, broadband acquisition also facilitates the capture of other frequencies of interest. Examples of such frequencies of interest may include QRS frequency maps (which may range up to 250Hz), and the like. The term "phase gradient" in relation to the acquired signals and the corresponding data set refers to signals acquired at different vantage points of the body to observe phase information for a set of different events/functions of the physiological system of interest. After signal acquisition, the term "phase gradient" refers to the preservation of phase information through the use of non-distorting signal processing and preprocessing hardware, software, and techniques (e.g., phase linear filters and signal processing operators and/or algorithms).

In a neurological context, the measurement system 102 is configured to capture a neurologically relevant biopotential or electrophysiological signal of a living body (e.g. a human) as a set of neurological biophysical signal data. In some embodiments, measurement system 102 is configured to acquire a broadband neural phase gradient signal as a biopotential signal or other signal type (e.g., a current signal, an impedance signal, a magnetic signal, ultrasound, an optical signal, ultrasound, or an acoustic signal, etc.). Examples of measurement system 102 are described in U.S. patent application publication nos. 2017/0119272 and 2018/0249960, each of which is incorporated by reference herein in its entirety.

In some embodiments, the measurement system 102 is configured to capture a broadband biopotential biophysical phase gradient signal as an unfiltered electrophysiological signal such that spectral components of the signal are not altered. Indeed, in such embodiments, the broadband biopotential biophysical phase gradient signal is captured, converted, or even analyzed (e.g., by, for example, hardware circuitry and/or digital signal processing techniques, etc.) without being filtered (which, if filtered, would affect the phase linearity of the biophysical signal of interest) (e.g., prior to digitization). In some embodiments, the broadband biopotential biophysical phase gradient signals are captured at a microvolt or sub-microvolt resolution at or significantly below the noise floor of conventional electrocardiography, electroencephalography, and other biophysical signal acquisition instruments. In some embodiments, the broadband biopotential biophysical signals are sampled simultaneously with a time offset or "lag" of less than about 1 microsecond, and in other embodiments, with a time offset or lag of no more than about 10 femtoseconds. Notably, the exemplary system minimizes non-linear distortions (e.g., those that may be introduced via certain filters) in the acquired wideband phase gradient signal so as not to affect the information therein.

Still referring to fig. 1A, the evaluation system 110 is configured to receive the acquired biophysical signal dataset 108 (in this embodiment, the denoised dataset 108) over, for example, a network and, in some embodiments, generate one or more three-dimensional vectorcardiogram datasets 122 by a transformation operation 120 (labeled "phase-space transformation" 120) for analysis via, for example, one or more machine learning analysis operations and/or one or more predictor operations (shown as step 124) of the phase gradient biophysical signal dataset 108. Examples of transformation operations and machine learning/predictor operations are discussed below and in U.S. patent application publication No. 2013/0096394, which is incorporated herein by reference in its entirety. In some embodiments, the acquired biophysical signal dataset 108 is constructed as a multi-dimensional dataset for subsequent processing without explicit transformation; for example, in case the intermediate data set is not visualized.

In some embodiments, the measurement system 102 is configured to evaluate the signal quality of the acquired biophysical signals and reject some or all of the acquired signal data set based on such evaluation. Fig. 1B is a diagram of an exemplary system configured to reject an acquired biophysical signal based on a quality assessment of the acquired biophysical signal by quantification of asynchronous noise and artifact contamination, according to another exemplary embodiment. In some embodiments, the measurement system 102 is configured to perform the asynchronous noise removal operation 118 and the signal quality assessment operation 130 based on the quantization of the asynchronous noise.

Because in some embodiments, the clinical analysis of the acquired biophysical signals 108 may be performed on a system separate from the measurement system 102 (e.g., the evaluation system 110), the signal quality check ensures that the acquired biophysical signal dataset 108 is suitable for subsequent clinical analysis. This operation may help prompt the non-invasive measurement system 102 for re-acquisition of the biophysical signal dataset, thereby ensuring that the acquired biophysical signal dataset is not contaminated with asynchronous noise (e.g., skeletal muscle related noise) before it is subjected to or used for further processing and analysis for clinical evaluation.

In some embodiments, the signal quality assessment operation 130 is performed in near real-time (e.g., in less than about 1 minute or less than about 5 minutes), in response to which the system 102 may prompt the reacquisition of the biophysical signal data set. This near real-time assessment allows the bio-physical signal data set to be re-acquired before the patient leaves the examination room or other location where the bio-physical signals are acquired, if desired. In some embodiments, the analysis performed by the evaluation system 110 to determine the clinical output requires approximately 10-15 minutes to perform. In other embodiments, the analysis needs to be performed for less than about 5 minutes. In further embodiments, the analysis may take approximately 5-10 minutes to perform. In yet another embodiment, the analysis is performed for more than about 15 minutes.

In some embodiments, the entire signal evaluation is conducted at the same physical location as the patient (e.g., on one or more computing devices and/or storage devices located in the patient's bedroom or clinician examination room). In some embodiments, the entire signal evaluation is conducted at a different physical location than the physical location of the patient (e.g., on one or more computing devices and/or storage devices located in another room, another building, another state, another country, etc.). In some embodiments, signal evaluation is performed in a networked environment involving multiple physical locations and multiple computing devices and/or storage devices. Such a networked environment may be ensured to protect the privacy of the patient whose signals are being evaluated, for example, to comply with various privacy requirements.

In some embodiments, signal evaluation is performed as signals are acquired from the patient-e.g., as fast or nearly as fast as the signal evaluation system is capable of operating (e.g., real-time or near real-time, depending on the signal evaluation system configuration, network limitations, etc.). In other embodiments, the signal evaluation is performed in part at the time of signal acquisition and in part after the signal is acquired from the patient and stored. In other embodiments, no signals are evaluated at the time of acquisition from the patient, but the signals are stored for evaluation at a time later relative to the time they were acquired from the patient. Of course, all signals (whenever they are evaluated) may be stored after acquisition for later evaluation or re-evaluation.

One or more clinicians may perform a clinical assessment of a patient based in whole or in part on the patient's signal assessment performed by the system through the methods described herein. Such clinicians may be physically with and/or physically remote from the patient. The signal evaluation systems described herein may also perform clinical evaluations of a patient, in whole or in part, by way of, for example, clinical outputs of one or more operations performed by the signal evaluation system. Alternatively, the signal evaluation system may simply provide information that is not accessible to clinical evaluation for use by clinicians in performing their own clinical evaluation of a patient. And where the signal evaluation system does provide clinical outputs, clinicians may also elect to accept or reject such clinical outputs when performing their own final clinical evaluation of the patient, for example where such clinicians' participation and final decision making is desired or even required (e.g., by law, agreement, insurance requirements, etc.).

In some embodiments, the non-invasive measurement system 102 is configured to generate a notification 126 (labeled "show failed signal quality assessment" 126 in fig. 1B) notifying acquisition failure or inopportune of the biophysical signal data set, wherein the notification may also prompt re-acquisition of the biophysical signal. The notification may be in any form, such as a visual output (e.g., one or more indicator lights or indicators on a screen), an audio output, a tactile/vibratory output (or any combination thereof) provided to a technician or clinician and/or patient. Examples of user interfaces (e.g., graphical user interfaces) of the measurement system 102 that may present the notification 126 are provided, for example, in the following documents: U.S. patent application publication No. 2017/0119272 entitled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition" filed on 26.8.2016; U.S. design with application number 29/578,421 entitled "Display with Graphical User Interface," each of which is incorporated herein by reference in its entirety. To this end, all or a portion of the rejected biophysical signal data set may not be used for subsequent analysis (e.g., 120, 124) to produce the clinical output 112.

In some embodiments, the rejected biophysical signal data set is optionally stored in any suitable memory (128) for further (troubleshooting) analysis (132) of defects and/or other causes that result in rejection of the acquired signals. To this end, all or a portion of the rejected biophysical signal dataset may not be used for subsequent analysis (e.g., 120, 124) to produce the clinical output 112, depending on the results of any such analysis 132.

In some embodiments, the system 200 may use all or a portion of the rejected biophysical signal dataset in subsequent analyses (e.g., 120, 124) to produce the clinical output 112 or, for example, to improve the operational capabilities of the system 200, or the like.

In other embodiments, a clinician or other operator may control whether and how all or part of the rejected biophysical signal data set is used, either alone or in conjunction with the system 200 or with the assistance of the system 200.

FIG. 2 is a diagram of an illustrative evaluation system 200 in accordance with an exemplary embodiment. In system 200, the different components of a Coronary Artery Disease (CAD) assessment algorithm are assembled to provide an assessment of CAD. This begins with the receipt of a signal and terminates with a final evaluation of the returned CAD, the location of the CAD relative to the affected artery, and/or a collection of one or more empty discontinuous layer cine data sets/images (also referred to as "PST data sets/images"). Thus, the system 200 may be used to determine whether a lesion is present, for example, in any coronary artery of a subject. For example, if coronary artery disease is determined and the presence of the disease can be located to the appropriate coronary artery, CAD location assessment can also be provided, such as Left Circumflex (LCX), Left Anterior Descending (LAD), Right Coronary Artery (RCA), other arteries, or some combination thereof. Further, a PST data set/image (e.g., a two-dimensional or three-dimensional graphical representation of the evaluation) may be generated and output by phase space analysis.

Examples of useful phase space concepts and analyses are described in the following documents: U.S. patent application publication No. 2018/0000371 entitled "Non-innovative 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 Assembly Disease Using Phase Space volume Objects" filed on 26.12.2018; U.S. patent application publication No. 2019/0200893 entitled "Method and System to Assembly use Phase Space Tomography and Machine Learning", each of which is incorporated herein by reference.

In some embodiments, the system 200 includes a healthcare provider portal (also referred to herein as a "portal") configured to display stored phase space datasets/images and/or clinical output 112, such as an assessment of the presence and/or absence of disease in phase space analysis and/or angiographic equivalent reports and/or estimated physiological characteristics of the physiological system under study (as well as other intermediate datasets). The healthcare provider portal, which may be referred to as a physician or clinician portal in some embodiments, is configured to access, retrieve, and/or display or present reports and/or facies spatial volume datasets/images and/or clinical outputs 112 (and other data) for reporting from a repository (e.g., a storage area network).

In some embodiments, the healthcare provider portal is configured to display the facies spatial volume dataset/image (or intermediate dataset derived therefrom) and/or the clinical output 112 in or along with the anatomical mapping report, coronary artery tree report, and/or 17-segment report. The healthcare provider portal may present the data, for example, in real-time (e.g., as a web object), in 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 or phase space volume datasets/images or clinical outputs (and other data) for reporting from a repository (e.g., storage area network). The healthcare provider portal and/or repository may comply with patient information and other personal data privacy laws and regulations (e.g., the 1996 U.S. health insurance flow and accountability act and the european union general data protection regulations) and laws related to the marketing of medical devices (e.g., the U.S. federal food and drug act and the european union medical device regulations). 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. Although in some embodiments, the healthcare provider portal is configured to present patient medical information to a healthcare professional, in other embodiments, the healthcare provider portal may be accessible to patients, researchers, scholars, and/or other portal users.

In some embodiments, the anatomical mapping report 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 a set of one or more three-dimensional views and/or a set of two-dimensional views of the model with the identified myocardial layer regions. In some embodiments, the coronary tree report includes one or more two-dimensional views of the major coronary arteries. In some embodiments, the 17-segment report includes one or more two-dimensional 17-segment views of the corresponding region of the myocardium. In each report, a value indicating the presence of a heart disease or condition at a location in the myocardium and a label indicating the presence of a heart disease may be rendered as static and dynamic visualization elements indicating a predicted region of obstruction (e.g., by color highlighting of the affected myocardium region and by an animation sequence that highlights the affected coronary artery region). In some embodiments, each report includes a textual label indicating the presence or absence of cardiac disease (e.g., the presence of significant coronary artery disease) and a textual label indicating the presence (i.e., location) of cardiac disease in a given coronary artery disease.

In some embodiments, in the context of the cardiovascular system, the healthcare provider portal (and corresponding user interface) is configured to present a summary information visualization identifying myocardium at risk and/or myocardium tissue of occluded coronary arteries. The user interface may be a graphical user interface ("GUI") with a touch or pre-touch sensitive screen having input capabilities. For example, the user interface may be used to guide diagnosis and treatment of a patient and/or to evaluate a patient in a study. For a given study report, the visualization may include a plurality of depictions: a rotatable three-dimensional anatomical map of the cardiac region of the affected myocardium, a corresponding two-dimensional view of the primary coronary artery, and a corresponding two-dimensional 17-segment view of the primary coronary artery to facilitate interpretation and assessment of structural features of the myocardium to characterize abnormalities of cardiac and cardiovascular function.

In the embodiment shown in fig. 2, the performance of the evaluation system 200 is gated at its beginning by signaling a requirement for sufficient quality for subsequent evaluations, as further described in some embodiments. In other embodiments of system 200, the signal quality requirement may be optional (e.g., selectively activated by system 200 and/or a user) or not present at all.

In the system 200, a Phase Signal Recorder (PSR) signal 205 and data indicative of the gender and/or age of a subject 210 (e.g., a patient) may be received as inputs. In one embodiment, the PSR signal 205 (and its corresponding data set) is an unmodified signal, which is an example of a wideband phase gradient biophysical signal. In a particular implementation, data from three channels, referred to as "ORTH 1", "ORTH 2", and "ORTH 3" (associated with signal 205) is parsed out in a PSR file downloaded from a storage area network (e.g., referred to as a "phase signal data repository" (PSDR)). As shown schematically in fig. 1A and 1B, a set of six probes or electrodes (e.g., probes 114a-114f) is positioned on subject 106. These electrodes 114 may be arranged, for example, along three orthogonal axes of the subject's body. In one implementation, the data from channel "ORTH 1" corresponds to a bipolar acquisition channel data series recorded by a phase space recorder from electrodes 114 placed along or near one of the orthogonal axes of the subject that passes through the subject's body from the left to the right of the subject. The data from channel "ORTH 2" corresponds to a second series of bipolar acquisition channel data recorded by the phase space recorder from two other electrodes 114, the two other electrodes 114 being placed along or near a second one of the orthogonal axes through the subject's body from the upper side to the lower side of the subject. And the data from channel "ORTH 3" corresponds to a third bipolar acquisition channel data series recorded by the phase space recorder from two further electrodes 114, the two further electrodes 114 being placed along or near a third of the orthogonal axes passing through the subject's body from the front to the back of the subject. The signals of ORTH1, ORTH2, and ORTH3 and their corresponding data sets may be arranged in mutually orthogonal axes, e.g., in phase space coordinates. Note that in some embodiments the ORTH1, ORTH2, and ORTH3 signals are referenced (e.g., as examples of wideband phase gradient biophysical signals) to more clearly distinguish them from vector cardiogram devices.

The evaluate signal quality system module 300, which is included in the measurement system 102 of fig. 1 to perform, for example, operation 130, will be further described in some embodiments with reference to fig. 3-21. Evaluating signal quality the system module 300 evaluates an input signal 383 (e.g., the PSR signal 205) to determine whether subsequent processing can be performed, or whether the signal is unacceptable (e.g., too noisy) and whether another input signal is acquired before subsequent processing can be performed. Thus, in the illustrated embodiment, the output of the evaluate signal quality system module 300 is an evaluation of whether to proceed with further analysis (e.g., as performed by the intermediate processing component 220 and 240 as seen in FIG. 2) and a determination of a time window for the signal suitable for analysis. If the evaluation indicates that: for example, if the collected PSR signal 205 is so suitable (i.e., acceptable), then the PSR signal, its corresponding data set, and the determined time window are provided for subsequent processing by the intermediate processing component; otherwise, the evaluate signal quality system module 300 indicates (e.g., to a user) that the acquired PSR signal (e.g., signal 205) is not suitable (i.e., not acceptable) and that another PSR signal must be acquired for subsequent processing. As described above, in other embodiments, the unacceptable signal quality indication presented by module 300 may be overridden by system 200 and/or a user (override), and some or all of input signal 383 may be used in subsequent processing. Further, whether all or part of the input signal 383 is used in conjunction with the evaluate signal quality system module 300, the signal 383 optionally can be stored in whole or part in memory for analysis and/or future use.

The intermediate processing components in the fig. 2 embodiment include a generate CNN (convolutional neural network) prediction module 220, a generate visual prediction module 225, a generate metadata prediction module 230, a combined prediction module 235, and a CVMGD (cyclic variability mediated sex correlation) compensation module 240. Some of all of these intermediate processing components or modules may or may not be present in other configurations of the system 200, as contemplated in other embodiments.

The generate CNN prediction module 220 receives as input the unmodified PSR signal 205 (the channel data from ORTH1 and ORTH3 parsed from the PSR file downloaded from the PSDR) and the time window of the signal suitable for analysis from the evaluate signal quality system module 300. In one exemplary implementation, the output of the generate CNN prediction module 220 is using, for example, two separate CNN models (e.g., CNNModel)₁₂₉And CNNModel₈₅) The overall CAD evaluation of the weighted sum of (a). Other embodiments of system 200 contemplate the use of one CNN or more than two CNNs.

The generate visual prediction module 225 receives as input the unmodified PSR signal 205 (the channel data from ORTH1, ORTH2, and ORTH3 parsed from the PSR file downloaded from the PSDR) from the evaluate signal quality system module 300 and outputs an overall CAD evaluation (hereinafter "visual feature evaluation") by extracting visual features and applying linear equations on these visual features.

The generate metadata prediction module 230 receives as input the unmodified PSR signal 205 from the evaluate signal quality system module 300. The module 230 also receives as input data 210 relating to or indicative of the gender (i.e., physiological gender) and/or age of the subject. Module 230 outputs an overall CAD estimate of the subject (referred to as "metadata" in the following exemplary equation) by using a linear formula that takes into account gender and/or age data.

In the embodiment of fig. 2, the combined prediction module 235 receives as input the overall CAD evaluation from the generate CNN prediction module 220, the overall CAD evaluation from the generate visual prediction module 225, and the overall CAD evaluation from the generate metadata prediction module 230.

The output from the combined prediction module 235 includes an intermediate continuous (non-binary) overall CAD estimate (referred to as an "intermediate CAD estimate" in the following exemplary equation). Details of how the combined prediction module 235 operates are provided below.

Z-score normalization is a statistical technique that centers the distribution on zero and scales the distribution so that the standard deviation of the distribution is 1, the output of this process is called "Z-score". As used in some embodiments, z-score normalization is used to ensure that the assessments (e.g., from 220, 225, 230) are comparable (e.g., each have the same mean and standard deviation) so that they can not only be combined with a averaging operation, but when all assessments are combined, one or more of the assessments do not affect another or the other assessments in an undesirable manner (e.g., dominate), so that each assessment is uniformly weighted and/or distributed. In other embodiments, the z-score normalization may be modified, used differently or in conjunction with additional processing, used not at all, or replaced entirely by one or more other techniques to give, for example, a preferred weight and/or distribution of one or more evaluations as needed.

In one implementation, the "Combined CNN Assessment" (which in this example has been two separate CNN models (CNNModels) from component 220₁₂₉And CNNModel₈₅) Weighted sum) and the output of the component/module 230 overall CAD (metadata) evaluation are added with weights 1 to 3(CNN to metadata) as shown in equation 1. And then tested by removing the pre-calculated constant ('s) expressed as the average of the N411 Stage I PSR test set "N411 _ CNN _ Mean ") and this normalized value is divided by a second pre-calculated constant (" N411 _ CNN _ SD ") representing the standard deviation of the same set; the result of performing these two operations is the Combined CNN Association's z-score, as shown in equation 2.

Combined CNN Assessment＝(CNNModel₁₂₉×1)+(CNNModel₈₅×2)]+ (metadata × 3) (Eq. 1)

In this implementation, the CAD evaluations output by the generate visual prediction module 225 are merged with the CAD evaluations output by the generate metadata prediction component 230 such that the average of these outputs is for females (equation 3), but only the output of the metadata prediction module 230 is for males (equation 4). The female and male "Visual Feature Assessment" values are then merged, e.g., by a set of merge operators U (according to equation 5) to provide a new set comprising members of the first set of females and the second set of males. These combined values are then normalized by removing the pre-computed constant ("N411 _ VF _ Mean") representing the average visual feature assessment of the N411 Stage I PSR test set. The resulting normalized value is then divided by a second pre-calculated constant ("N411 _ VF _ SD") representing the standard deviation of the same set. The result of performing these two operations is z-score as shown in equation 6.

Visual Feature Assessment_MaleMeta (equation 4)

Visual Feature Assessment_{Female∪Male}＝Visual Feature Assessment_female∪Visual Feature Assessment_male(equation 5)

Continuing with the exemplary implementation of the embodiment, the outputs from the CNN evaluation (i.e., z-score according to equation 2) and the visual feature evaluation (i.e., z-score according to equation 6) are then averaged to create an "Intermediate CAD evaluation (Intermediate CAD Association)" value as the final output of the combined prediction component 235, as shown in equation 7. In other words, module 235 sums the outputs of these z-scores for each patient, then divides the sum by 2:

continuing with this implementation of the embodiment, CVMGD compensation module 240 receives as inputs gender and/or age data 210 of the subject and the intermediate CAD evaluation output from combination prediction module 235, and outputs a final continuous (non-binary) CAD evaluation value, which may be referred to as a "final continuous CAD evaluation". Component 240 utilizes the concept of cycle variability scores and computes maximum variability scores for ORTH1 and ORTH3 (and, in some embodiments, ORTH 2). The cycle variability score captures information about the variation between cardiac cycles and, within a given range of the score, captures electrophysiological variability that embeds the information content of the CAD state, which was observed to be particularly suitable for male subjects. The compensation operation implemented in module 240 utilizes this cycle variability information of the male subject to improve the overall disease assessment (e.g., as provided by the visual feature assessment and machine learning assessment). In practice, CVMGD compensation module 240 operates on a potential basis as follows: within a given range of variability of the cycles, the positive CAD assessments of the male subjects output from the combination prediction component 235 are more likely to actually be negative CAD assessments, and conversely, the negative CAD assessments of the male subjects generated by the combination prediction component 235 are more likely to be CAD positive assessments.

In some embodiments, CVMGD compensation module 240 is configured to determine (i) whether the cycle variability score of the male subject (e.g., calculated by module 300) is within a predetermined range (e.g., between 0.0071 and 0.0079) and (ii) whether the intermediate CAD evaluation value for the given male subject output by module 235 is greater than or equal to a predefined "threshold" (e.g., stored in module 240) plus X. If both conditions are met (e.g., a positive CAD evaluation for a male subject with a particular cycle variability characteristic), the CVMGD compensation module 240 is configured to determine a final consecutive CAD evaluation value for the male subject, outputting a final consecutive CAD evaluation score as a threshold minus an amount X (i.e., providing a negative CAD evaluation), where X is determined as the difference between the threshold minus the intermediate CAD evaluation score and the threshold. That is, the CVMGD compensation module adjusts the intermediate CAD evaluation value determined by equation 7 to a magnitude equal or equivalent to the threshold value minus X. Instead, CVMGD compensation module 240 is configured to determine whether both of the following are satisfied: (i) the variability score of the period of the male subject (stored in module 300) is within a predefined range of the same variability score of the period, and (ii) the intermediate CAD evaluation value is less than the same predefined threshold by a magnitude of Y. If both conditions are met (negative CAD evaluation for male subjects with particular cycle variability characteristics), CVMGD compensation module 240 is configured to determine the final consecutive CAD evaluation score as a threshold plus an amount Y (i.e., providing a positive CAD evaluation), where Y is determined as the difference between the threshold plus the threshold and the intermediate CAD evaluation score. That is, the CVMGD module adjusts the intermediate CAD evaluation value to a magnitude equal or equivalent to the threshold value plus Y. If the subject does not satisfy either of the above, the subject's score is not modified by module 240 and the original value is passed without change. If so, this change embeds the following information: as previously specified, within a given range of variability of cycles, a positive CAD assessment for a male (as output by the combination prediction component 235) is more likely to be negative, and conversely, a negative CAD assessment for a male is more likely to be positive.

In an example of module 240 with respect to the embodiment of fig. 2, consider a male subject that satisfies the following two conditions: (i) an intermediate CAD evaluation score of 0.14460082598168, a cycle variability score (maximum of ORTH1 and ORTH3) within predetermined (cycle variability) thresholds of 0.0071 and 0.0079 (greater than 0.0071 but less than 0.0079), and (ii) a predefined (CAD evaluation) threshold of 0.13460082598168: in this case, the CAD evaluation would be modified by the compensation operation performed by module 240 such that the subject is assigned 0.124600825981680 an intermediate CAD evaluation score (e.g., calculated as 0.13460082598168 minus the difference between the previous intermediate CAD evaluation score 0.14460082598168 and the threshold 0.13460082598168). The intermediate CAD evaluation score (previously above the threshold) is now adjusted to be below the threshold because the resulting final consecutive CAD evaluation score is now below the threshold 0.13460082598168 by the difference between the threshold and the input intermediate CAD evaluation score. The modified score is then output from module 240. Indeed, a positive CAD assessment generated by the combined prediction component 235 for a male subject is now output as a negative assessment.

The outputs of the evaluate signal quality module 300 and the CVGMD compensation component 240 are used differently in subsequent terminal processing performed by various terminal blocks or modules/components, which in the system 200 of fig. 2 include a final CAD prediction module 250, a generate location prediction module 255, and a generate facies space volume dataset/image module 260 (shown as "generate PST" 260).

Final CAD prediction module 250 uses the final successive CAD estimates from CVMGD compensation component 240 and provides the final binary CAD estimates as output. In one implementation, if the final consecutive CAD assessment is greater than or equal to threshold 0.13460082598168, the subject is predicted to be CAD positive; otherwise, the subject is predicted to be CAD negative. The accuracy of the threshold (e.g., 0.13460082598168) is 14 significant digits, since the threshold generated in this example corresponds to a score that belongs to a particular subject and can be used to identify the source of the threshold during analysis and development. The value of such a threshold may be used in other embodiments with various other accuracies.

The generate location prediction module 255 uses the final binary CAD estimate output from the final CAD prediction module 250 and the binary location estimate of the subject's coronary arteries (e.g., LCX, LAD, RCA) as inputs and outputs the CAD location estimate.

In some embodiments, the generate PST module 260 uses the unmodified PSR signal 205 (via the evaluate signal quality system 300) and outputs a PST data set/image. In other embodiments, the generate PST module 260 uses PSR signals pre-processed by other modules in the evaluation system 110.

Fig. 3 is a diagram illustrating a signal quality evaluation system 300 according to an exemplary embodiment, and fig. 4 is an operational flow diagram of an implementation of a method 400 of evaluating signal quality according to another exemplary embodiment. The components of the signal quality assessment system 300 combine to create an efficient method for assessing signal quality that allows for subsequent analysis. The signal quality assessment method 400 may be used in a gating phase for subsequent analysis.

The system 300 includes a signal quality evaluator 305 and a measurement system 380. Signal quality evaluator 305 and measurement system 380 may be included within the same computing device or, as with all components of the systems described throughout this disclosure, may be included in separate computing devices that are in communication with each other (e.g., directly connected or coupled to each other, or communicatively connected or coupled to each other through a wired, optical, or wireless network). The network may be comprised of one or more of a variety of network types, including Public Switched Telephone Networks (PSTN), cellular/mobile telephone networks, local area networks (e.g., wired or wireless ethernet (LAN)), networks including Near Field Communication (NFC) or other radio frequency-based technologies and standards (e.g., bluetooth low energy, etc.), packet-switched networks (e.g., the internet), and so forth. Although only one measurement system 380 is shown in fig. 3, the number of measurement systems 380 that can be supported is not limited. Signal quality evaluator 305 and measurement system 380, like all components of the systems described throughout this disclosure, may each be implemented using one or more processors in conjunction with any kind of computing device, such as a smartphone, smartwatch, desktop computer, server computer, mainframe computer, laptop computer, tablet computer, and set-top box (including any combination thereof). Other types of computing devices may be supported. A suitable computing device is illustrated in fig. 22 as computing device 2200.

Measurement system 380 may be any measurement system, such as measurement system 102, and signal quality evaluator 305 may be implemented separately or within measurement system 102.

The quality of any electrical signal, including the quality of biophysical signals, such as the PSR signals described herein (e.g., signals acquired at about 8 kHz), may be affected by noise, which may originate from a variety of sources. This noise can affect the quality of the acquired signal in a number of ways. For example, noise can negatively impact the performance of subsequent analyses, such as those described herein, relating to a clinical indication or disease state of a subject or patient. The impact of negative performance of subsequent analyses can be manifested in a number of ways. When processing is performed remotely (e.g., in a cloud service), real-time or near real-time rejection of signals facilitates patient re-acquisition or re-measurement, reducing the inconvenience and cost of a patient having to return to, for example, a doctor's office, hospital, or other clinical environment to re-acquire signals. Furthermore, if the subsequent analysis involves generating a data set/image (e.g., a phase space data set/image) for interpretation by a physician, the image may not correctly represent the physiological state of the subject and thus lead to misinterpretation, possibly leading to delayed diagnosis and/or treatment or incorrect diagnosis and/or treatment. If the subsequent analysis involves a definite quantitative assessment of a given disease state in the subject, negative performance may involve incorrect quantification of the disease state, which may lead to delayed or incorrectly rejected treatment, or unnecessary use of additional testing or intervention measures, which may introduce the possibility of harm to the patient. Furthermore, regardless of the type of analysis, noise may extend processing time and/or cost (in computing resources) to produce output. Thus, it may be useful to identify and quantify noise so that the noise may be excluded, minimized, or otherwise processed, in whole or in part (or even in some cases along with the signals associated therewith), to eliminate or minimize such negative effects. Examples of noise relevant to the present disclosure include, for example, power line interference, high frequency noise bursts, (sudden) baseline movement, and cycle variability. For purposes of this disclosure, any unwanted interference in the signals disclosed herein (regardless of its source) may be considered "noise".

As seen in the exemplary method shown in fig. 3 and 4, the measurement system 380 acquires an input signal 383 (e.g., the PSR signal 205) and provides it to the signal quality evaluator 305, at an acquire signal step 410. The input signal 383 may be an acquired biophysical signal or biophysical signal dataset of the subject. For cardiac signals, a handheld device or other device may be used to collect the subject's resting chest physiological signals, e.g., from a single probe/sensor or a set of any number of probes/sensors or electrodes (e.g., six probes 114a-114f), which in the case of, e.g., six probes, are arranged along three orthogonal axes corresponding to the ORTH1, ORTH2, and ORTH3 channels as described above. Electrodes that are part of the non-invasive measurement system 102 can acquire phase gradient biophysical signals (derive the set 108 as described above) without the use of ionizing radiation, contrast agents, motion stressors, or pharmacological stressors; however, in some embodiments, the biophysical signal of interest may still be used in conjunction with such protocols or equipment. In some embodiments, the non-invasive measurement system 102 samples at about 8kHz for a duration between about 30 seconds and about 1400 seconds, preferably about 210 seconds. The collected data points are transmitted as part of the data set 108 to an evaluation system 110 and evaluated, for example, by an analysis engine therein using a machine-learned algorithm/predictor. Other electrode sets and electrogram (electrographic) acquisition methods may employ the scenarios of the methods and systems disclosed herein.

As further described herein, at step 420, signal quality evaluator 305 uses one or more of power line interference module 320, high frequency noise module 330, high frequency noise burst module 340, sudden movement module 350, and period-variant noise module 360, any or all of which are combined with decision module 310, to perform a quality evaluation and generate an output (e.g., output 386). The signal quality evaluator 305, and/or one or more of the power line interference module 320, the high frequency noise module 330, the high frequency noise burst module 340, the sudden movement module 350, the period variant noise module 360, and/or the decision module 310 may reside or be placed on or within a device or apparatus housing or comprising the measurement system 102 or the evaluation signal quality system 300, or be placed locally, remotely (e.g., a server/processor, software, and service residing and/or running in a "cloud" and communicating with a local server/processor via a network such as the internet), or be placed in a hybrid system/arrangement (where some quality evaluation evaluations are performed on the device and some quality evaluation evaluations are performed remotely). An example of a suitable device or apparatus is illustrated in fig. 22 as computing device 2200.

At step 430, an output 386 is provided to the measurement system 380 and the user. In one implementation, output 386 includes indicators for accepting or rejecting the acquired signals for subsequent processing and analysis (e.g., performed by intermediate and terminating processing components described with respect to fig. 2).

In one implementation, the input signal 383 is an unmodified PSR signal (channel data from ORTH1, ORTH2, and ORTH3 parsed from a PSR file downloaded from a PSDR (phase signal data repository)). As described above, the data from the channel "ORTH 1" corresponds to a bipolar acquisition channel data series recorded by the phase space recorder from the electrodes 114 placed along or near one of the orthogonal axes of the subject that passes through the subject's body from the left to the right of the subject. The data from channel "ORTH 2" corresponds to a second series of bipolar acquisition channel data recorded by the phase space recorder from two other electrodes 114, which electrodes 114 are placed along or near a second of these orthogonal axes passing through the subject's body from the upper side to the lower side of the subject. And the data from channel "ORTH 3" corresponds to a third bipolar acquisition channel data series recorded by the phase space recorder from two more electrodes 114, the electrodes 114 being placed along or near a third of the orthogonal axes through the subject's body from the front to the back of the subject. The signals of ORTH1, ORTH2, and ORTH3 and their corresponding data sets may be arranged in, for example, phase space coordinates, in mutually orthogonal axes.

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

As further described with reference to fig. 3 and 5-7, the power line interference module 320 detects or determines power line interference noise (e.g., noise introduced by a 60Hz power line (which frequency is commonly used in countries and regions such as canada, korea, the united states, some regions of japan and taiwan of china) and harmonics thereof; noise introduced by a 50Hz power line (which frequency is commonly used in regions such as china, france, germany, india, italy, swiss, the united kingdom, and some regions of japan) and harmonics thereof.

As further described with reference to fig. 3 and 8-10, high frequency noise module 330 detects or determines excessive (output) signal frequency content (e.g., above 170Hz in one implementation, where frequency content above 170Hz is not necessarily periodic and may include pulses and other such artifacts). The output of the high frequency noise module 330 is provided to the decision module 310 for processing, e.g., as further described herein.

The high frequency noise burst module 340 detects or determines the short bursts of excess high frequency content described above, as further described with reference to fig. 3 and 11-13. The output of the high frequency noise burst module 340 is provided to the decision module 310 for processing, e.g., as further described herein.

As further described with reference to fig. 3 and 14-16, the abrupt move module 350 detects or determines extreme baseline wander that is located in a particular segment of the signal, where the wander is significant enough to produce signal distortion. The output of the jerky movement module 350 is provided to the decision module 310 for processing, e.g., as further described herein.

As further described with reference to fig. 3 and 17-19, period variability noise module 360 provides quantification of noise asynchronous to the cardiac cycle, which may include voltage potentials generated by skeletal muscle activation. The output of the period variability noise module 360 is provided to the decision module 310 for processing, e.g., as further described herein. The concepts described herein may be applied to quantify noise asynchronous to other periodic physiological signals outside of the cardiac environment.

In one implementation, the evaluations described herein are performed at the channel level, e.g., evaluated independently on the ORTH1, ORTH2, ORTH3 channels, and merged at a post-processing stage.

Fig. 5 is an operational flow diagram of an implementation of a method 500 of evaluating power line interference according to another example embodiment. In one embodiment, the power line coefficient may be an indicator of power line noise pollution, with higher values indicating higher pollution.

At step 510, power line coefficients are calculated by switching from the time domain to the frequency domain using a Fast Fourier Transform (FFT). At 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 a base local frequency energy (e.g., base abs (mean (55 ≦ freq ≦ 58or 62 ≦ freq ≦ 65)) from average decibel values between 55Hz to 58Hz and between 62Hz to provide a baseline power by which to detect whether a peak occurs in the middle of the range (e.g., between 58Hz to 62 Hz). the method then determines a maximum power line energy (e.g., peak height base abs (mean (58 ≦ freq ≦ 62)) from a maximum decibel value between 58Hz and 62Hz to quantify the peak between 58Hz and 62 Hz.the method then determines a ratio of the peak height above base to the base (e.g.,). In effect, the determined ratio quantifies the presence of a deflection (or peak) at 60Hz in the FFT periodogram.

At step 530, the power line coefficients are further extended by a lower weighted addition including harmonics (from 60Hz) at 180Hz and 300Hz, which are calculated using the same method as described above with respect to the power line coefficients at 60 Hz. It is contemplated that other frequencies and harmonics may be used depending on the particular implementation. The output of this evaluation is each OThe powerline coefficients of the RTH1, ORTH2 and ORTH3 channels, which are basically named "powerlineCoeff" (modified only to indicate the data channel). At step 540, the output (i.e., power line coefficients) is determined as a fraction compared to a threshold to determine whether to reject the input signal based on power line interference. In some embodiments, the power line coefficient fraction is determined asIn one implementation, if the power line coefficient is greater than the threshold 486.6, the signal is rejected based on power line interference. In some implementations, geographic factors may be considered in quantifying the score; for example, with respect to the device being used. For example, for regions using 50Hz, the calculation of the score may be modified to quantify the 50Hz power line noise.

Note that the figures (graphs) (e.g., fig. 6, 7, 9, 10, 12, 13, 15, 16, 18, 19) are used here to help describe different types of noise and to share common types of tags; along the bottom axis is time (time points in samples) which reflects the number of data points in the 8kHz signal (8000 samples per second taken), and along the vertical axis is amplitude. The amplitudes in these figures are expressed in mV; however, in some implementations, a normalized amplitude may be used, which requires removing the mean of the signal and dividing by the standard deviation (thus converting the signal to z-score; e.g., the number of standard deviations of a data point from its mean, the sign representing the direction).

For example, fig. 6 is a graph 600 illustrating observable characteristics of maximum power line interference in a cardiac environment, such as the power line interference assessment operation of fig. 5, in accordance with another exemplary embodiment. Fig. 6 shows the maximum power line coefficient of the channel stage. Power line interference is clearly visible between ventricular depolarization signals, which oscillate periodically in the amplitude range of-9.15 mV to-9.45 mV.

Fig. 7 is a graph 700 illustrating observable characteristics of power line interference at a threshold, such as the method of power line interference assessment operation of fig. 5, in accordance with an example embodiment. Fig. 7 shows marginally acceptable power line coefficients, e.g., the first coefficient will be accepted and any higher coefficients will be rejected. That is, a power line coefficient of 60Hz was found to be acceptable in this data, but power line coefficients of 180Hz and 300Hz were found to be unacceptable.

FIG. 8 is an operational flow diagram of an implementation of an exemplary method 800 of evaluating high frequency noise according to an exemplary embodiment. The high frequency noise score is calculated by first performing a Stationary Wavelet Transform (SWT) at 810. Note that SWT is functionally similar to fast fourier transforms, except that it allows frequency localization in real time at the expense of frequency detail. At step 820, the energy of levels 9 to 13 corresponding to content greater than 170Hz is preserved, and then the inverse SWT is performed at step 830. The result of these transformations is high frequency content (which is visible if the intermediate output is plotted) back into the time domain.

At step 840, the signal-to-noise ratio (SNR) is calculated as the high frequency noise score by comparing the original signal with the noise extracted with SWT in the time domain. The output of this evaluation has the base name "hfNoiseToSignalRatio," which is only modified to indicate the source channel.

At step 850, the output (i.e., 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, if the high frequency noise score is greater than 0.05273, the signal is rejected based on the high frequency noise.

Fig. 9 is a graph 900 illustrating observable characteristics of maximum high frequency noise in a cardiac context, such as the high frequency noise assessment operation of fig. 8, in accordance with an example embodiment. Here, high frequency noise is visible, such as the thick black line indicated in fig. 9 partially surrounded by box 902. This noise in fig. 9 begins with a range of amplitudes from about 3mV to about 4mV and trends downward over time with a slow drift of the baseline from about-2 mV to about-4 mV.

FIG. 10 is a graph 1000 of high frequency noise at a threshold of the high frequency noise assessment operation of FIG. 8, according to an exemplary embodiment of a cardiac background. Here, the signal is just acceptable. The x-axis time scale is smaller than that of fig. 9, and the phase waveform is clearly discernable (e.g., QRS waveform, T waveform, etc.). In this particular example, high frequency noise is visible as pulses between ventricular repolarization excursions and atrial depolarization events. Noise is most prominent along the entire baseline, after ventricular repolarization (e.g., "T-wave"), but before atrial depolarization ("P-wave"), as shown at block 1010. Such noise occurs in every cycle.

Fig. 11 is an operational flow diagram of an exemplary implementation of a method 1100 of evaluating a high frequency noise burst. The scores generated by the method 1100 quantify the same high frequency noise as described with respect to fig. 8, but instead are localized to the one-second segment. The value of this fraction, together with the sudden or extreme baseline drift (described further herein) cannot by itself exclude signals from subsequent analysis; however, this can occur if the signal length is insufficient (e.g., at least 16 consecutive seconds, and the period of all channels is the same), and there are no high frequency noise bursts or sudden baseline shifts.

The score calculation associated with the exemplary method 1100 is similar to the overall high frequency score calculation described elsewhere herein. For example, high frequency components are extracted from the signal at steps 1110, 1120, and 1130 using the same or similar methods as described with respect to fig. 8 (similar steps 810, 820, and 830, respectively).

At step 1140, the illustrated exemplary process uses this high frequency time series data obtained in conjunction with step 1130 to test a one-second window and compare the result to the total median high frequency energy calculated in the full signal. At step 1150, in one implementation, if the energy obtained in a given one-second window or set of one-second windows is greater than twice the total median, then the segment is marked as containing high frequency noise. In one implementation, any single one-second window may be marked. For a set of one-second windows to be marked, all of these must meet the same criteria as the one-second window, i.e. twice the total median energy. Thus, the criteria for a set of windows are the same as the criteria for a single window.

In one implementation, the output from this evaluation is two scores that are modified to indicate the source data channel. For example, scores labeled "mediahfsignalenergy" (indicating the total median high frequency energy) and "hfNBCoefficient" (indicating 1 second energy for any window greater than four times mediahfsignalenergy) may be output. If there are multiple such windows, the maximum value is returned.

Fig. 12 is a graph 1200 illustrating observable characteristics of a maximum high frequency noise burst 1210 of a high frequency noise burst evaluation operation, such as that of fig. 11, in accordance with an example embodiment. In this case, the high frequency noise burst 1210 is characterized as a pulse that lasts several data points (e.g., less than 1 millisecond). As in the other figures (fig. 6, 7, 9, 10, 13, 15, 16, 18, 19), the x-axis is in samples and the y-axis is in mV (not normalized).

Fig. 13 is a graph illustrating another observable characteristic of a high frequency noise burst graph 1300, such as the high frequency noise burst evaluation operation of fig. 11, in accordance with an example embodiment. This is a more typical characteristic of high frequency noise bursts than depicted in fig. 12, where several high frequency bursts indicated at 1310 are observed within the indicated time frame.

Fig. 14 is an operational flow diagram of an implementation of a method 1400 of assessing sudden baseline movement according to an exemplary embodiment of a cardiac context. In this example, if the change in baseline (relative to baseline in the previous window) in a one-second window of the signal exceeds 25% of the ventricular depolarization amplitude of the channel, then the movement in baseline is defined as "abrupt". In this context, other definitions of "sudden" may be suitably determined depending on the type of biophysical signal acquired and analyzed, the physiological condition of the patient, and other factors.

At step 1410, a baseline in a one second window of the input signal is determined. At step 1420, a baseline in the next second window of the signal is determined. At step 1430, a score is determined based on the movement of the baseline between the two windows.

In one implementation, if the movement within the window is less than 25%, a fractional zero is assigned thereto. If there are more than 25% moves in a window of one second, then the maximum of these moves is assigned to the window. The output of this evaluation has the base name "maxabrupmovementpercentage" which is only modified to reflect the source data channel.

Fig. 15 is an illustration diagram 1500 illustrating observable characteristics of a maximum sudden movement of a subject, such as the observable characteristics of the sudden baseline movement assessment operation of fig. 14, in accordance with an exemplary embodiment of a cardiac context. Here, the phase signal initially settles at an amplitude of 100mV, but then the baseline value first drops by about 20mV and then drops substantially to around 30 mV. During these sudden baseline changes, the phase signal is either severely attenuated or absent.

Fig. 16 is an illustration graph 1600 illustrating observable characteristics of an abrupt movement of approximately 50% of the abrupt baseline movement assessment operation of fig. 14, for example, in accordance with an exemplary embodiment of a cardiac context. This is a more typical example of an abrupt baseline shift, shown here as 1610, which depicts an upward shift in signal amplitude followed by an equal downward shift, where the signal amplitude shift results in a ventricular depolarization event at the top of the shift having an amplitude that is about 50% higher than the ventricular depolarization event before the 1-second window.

Fig. 17 is an operational flow diagram of an implementation of a method 1700 of assessing cycle variability according to an exemplary embodiment in a cardiac environment. Examination of phase cycle variability (in the cardiac system and other physiological systems or combinations thereof) reveals asynchronous noise, which can quantify the presence of muscle noise artifacts and other types of noise that are inconsistent with the phase cycle.

The period variability noise may be calculated using the exemplary technique of fig. 17, which detects content that is within a band of the frequency range of the phase signal and has similar amplitude.

At step 1710, all phase cycles in the selected channel are detected by marking each ventricular depolarization event (e.g., the point in time during each phase cycle when electrical activation of the ventricles is maximal).

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

At step 1740, the technique compresses all the resulting differences of the detected phase periods to create the final period variability score for the channel.

In one implementation, a final cycle variability score is calculated for each of the channels orthi and ORTH3, and the larger of the two scores is used to generate an overall score for the signal. In this example, ORTH2 was excluded from this calculation because it was found to have the highest final cycle variability score-more than two-thirds of the time-much higher than one-third of the time (statistically expected if the final cycle variability of each of the three channels has the same highest likelihood) compared to ORTH1 and ORTH 3. Thus, the final cycle variability score comprising the signal obtained from the ORTH2 channel will disproportionately affect or push the value of the total score compared to the contributions of the other two channels. However, in an alternative implementation, the maximum value may be calculated from all three channels (ORTH1, ORTH2, and ORTH 3). This alternative maximum value is expected to be higher than the values obtained based on ORTH1 and ORTH 3; however, this technique imposes more stringent criteria on signal quality, which may be useful in some situations.

In human subjects in a cardiac context, the ORTH2 vector typically spans directly from directly under the subject's left clavicle to directly under the chest end as compared to the other pathways, potentially leading to two problems with signal acquisition, i.e., without wishing to be bound by theory, leading to a higher correlation of their final cycle variability scores, resulting in higher levels of correlated noise. First, termination of the ORTH2 vector directly below the chest places it in the stomach, and subjects receiving CAD assessment may tend to have a higher Body Mass Index (BMI) (BMI is a risk factor for CAD) than the general population. A higher BMI generally indicates the presence of excess abdominal fat, which changes the impedance between the two electrodes used for signal acquisition. Second, ORTH2 vectors are perpendicular to the stripes of the left pectoral muscle, which may increase the ability of the noise associated with the pectoral muscle to penetrate and/or otherwise affect the signal. Muscle noise comes from contractions; however, shrinkage does not necessarily mean movement. For example, isometric contraction is a static contraction of a muscle without any visible movement of the joint angle.

Fig. 18 is a graph 1800 illustrating observable characteristics of the highest cycle variability noise of the cycle variability evaluation operation of, for example, fig. 17, according to an exemplary embodiment in a cardiac environment. During the time period after the ventricular repolarization event is offset and before the atrial depolarization event, noise is most noticeable between phase cycles, which appear similar to an extra-connected (extra) phase waveform (e.g., extra ventricular repolarization event). An example of such noise is indicated by block 1810. Note that noise occurs in every cycle and is most pronounced between heartbeats of the subject (e.g., QRS waveforms appear as "spikes").

Fig. 19 is a graph 1900 illustrating observable characteristics of lower cycle variability noise, such as the cycle variability assessment operation of fig. 17, according to an exemplary embodiment in a cardiac environment. This figure shows a lower level of noise pollution compared to the noise signature of, for example, figure 18. Other differences can also be seen here, such as noise having a relatively low frequency and a relatively high amplitude range, the latter resulting in a more pulse-like character in the example of fig. 19.

Fig. 20 is an operational flow diagram of an implementation of a method 2000 of assessing signal quality according to an exemplary embodiment. In particular, the scores are evaluated in the signal availability workflow described with respect to fig. 20.

At step 2005, an input signal (e.g., PSR signal 205) is received.

At 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 greater than the threshold, the input signal is rejected at step 2060. Otherwise, the input signal is deemed to pass the test, and in this example, a binary number indicating that test #1 passed is provided to the boolean and operator.

At step 2015, another high frequency noise test (test #2) is performed to determine if the high frequency signal-to-noise ratio is greater than a threshold. If greater than the threshold, the input signal is rejected at step 2060. Otherwise, the input signal is deemed to pass this test #2, and in this example, a binary number indicating that test #2 passed is provided to the boolean and operator.

At steps 2020, 2025, and 2030, a jerk test (collectively referred to as "test # 3") is performed to determine if a clean signal segment is not available due to jerk and a high frequency noise burst. If any one or more of the component tests of test #3 for each of steps 2020, 2025 and 2030 indicate that such a clean signal segment is not available, the input signal is rejected at step 2060. Otherwise, the input signal is deemed to have passed each of the tests of steps 2020, 2025 and 2030, such that the input signal is test #3, and a binary number indicating the passed test is provided to the boolean and operator.

At step 2030, a cycle variability test (test #4) is performed on the input signal to determine if there is cycle variability noise greater than a threshold (e.g., on ORTH1 or ORTH 3). If so, the input signal is rejected at step 2060. Otherwise, the input signal is considered to pass this test #4, and a binary number indicating that the test #4 is passed is provided to the boolean and operator.

After the test is complete and the binary numbers have been provided to the boolean and operator, the operator performs a boolean operation based on the inputs at step 2040 and accepts the input signal at step 2070 if all of the input binary numbers indicate that the test passed. The logic specifies that: all tests must pass (step 2070) in order for the signal to pass and be accepted. If any of the tests fail, the signal is rejected (step 2060).

Thus, for any given signal: (1) rejecting the patient's input signal if any of the patient's or subject's channels fail the powerline interference metric test #1 at step 2010; (2) rejecting the input signal of the subject if any channel of the subject fails the high frequency noise metric; (3) rejecting the patient's input signal if the patient's ORTH1, ORTH2, and/or ORTH3 channel fails the cycle variability threshold; (4) if sudden movements or high frequency noise bursts are detected in the input signal of the patient, an attempt is made to find at least one such 16-second signal segment: where no sudden movement or noise burst is detected and the segment is the same for all channels (e.g., all channels are clean during the segment), rejecting the patient's input signal if no such window is found, otherwise (i.e., if such a window is found), processing the patient's input signal using the found window; (5) if all channels of the patient input signal pass all metrics, then the subject's input signal is processed normally. This is represented in FIG. 20 as the "Boolean AND" operator 2040 (i.e., all tests must pass).

An exemplary table of tests and thresholds for implementation is provided in table 1.

In north america, for the detection of power line frequencies (e.g., 60Hz is typically used; other regions, such as china, the european union, india, etc., typically use 50Hz instead of 60Hz), it may be necessary to modify the score to target a particular frequency.

Fig. 21A and 21B show an architecture and data flow diagram 2100 illustrating a signal quality assessment component in accordance with an example embodiment.

Note that failure of any test in the illustrated example does not prevent subsequent execution of the evaluation system 200; instead, a failure may be reported and an attempt made to perform a PST evaluation by the following mechanism.

The input may contain unmodified ORTH1, ORTH2, ORTH3 data parsed from the facies space recorder file.

The output may be passed to a data transfer api (dtapi) and a Report Database (RD). In one implementation, the legacy fields "noiseVolume" and "noiseLevelMean" are reused to maintain backward compatibility in the DTAPI and RD components. In certain embodiments, the report database is configured to store the noiseVolume parameter as the period variability score if all other signal quality assessment tests pass. If any test (other than cycle variability) fails, a 10000 flag is used to indicate the state. In an implementation, the threshold value of the period variability may be 0.0106.

For example, in some embodiments, the noiseLevelMean stores a state code with possible states as shown in table 2.

TABLE 2

FIG. 22 shows an exemplary computing environment in which example embodiments and aspects, such as evaluation system 110, signal quality evaluator 305, may be implemented in accordance with an example embodiment. The computing device environment is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality.

Many other general purpose or special purpose computing device environments or configurations may be used. Examples of well known computing devices, environments, and/or configurations that may be suitable for use include, but are not limited to: personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network Personal Computers (PCs), minicomputers, mainframe computers, embedded systems, distributed computing environments that include any of the above systems or devices.

Computer-executable instructions, such as program modules, executed by a computer may be used. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Distributed computing environments may be used where tasks are performed by remote processing devices that are linked through a communications network or other data transmission medium. In a distributed computing environment, program modules and other data may be located in both local and remote computer storage media including memory storage devices.

With reference to fig. 22, an example system for implementing aspects described herein includes a computing device, such as computing device 2200. In its most basic configuration, computing device 2200 typically includes at least one processing unit 2202 and memory 2204. Depending on the particular configuration and type of computing device, memory 2204 may be volatile (such as Random Access Memory (RAM)), non-volatile (such as Read Only Memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in fig. 22 by dashed line 2206.

Computing device 2200 may have additional features/functionality. For example, computing device 2200 may include additional storage (removable and/or non-removable) including, but not limited to, magnetic or optical disks or tape. Such additional storage is illustrated in fig. 22 as removable storage 2208 and non-removable storage 2210.

Computing device 2200 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by device 2200 and includes both volatile and nonvolatile media, removable and non-removable media.

Computer storage media includes volatile and nonvolatile, and removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Memory 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 programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computing device 2200. Any such computer storage media may be part of computing device 2200.

Computing device 2200 may contain communication connections 2212 that allow the device to communicate with other devices. Computing device 2200 may also have input device(s) 2214 such as keyboard, mouse, pen, voice input device, touch input device, etc. (alone or in combination). Output devices 2216 (e.g., a display, speakers, printer, vibrating mechanism, etc.) may also be included, either alone or in combination. All of these devices are well known in the art and need not be discussed at length here.

It should be appreciated that the various techniques described herein may be associated with hardware components or software components, or implemented by a combination of both, where appropriate. Example types of hardware components that may be used include Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like. The methods and apparatus of the subject matter of the present disclosure, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the subject matter of the present disclosure.

Although the illustrated implementations may refer to the use of the subject matter of the present disclosure in the context of one or more stand-alone computer systems, the subject matter is not so limited, but may be implemented in any computing environment, such as a network or distributed computing environment. Moreover, various aspects of the presently disclosed subject matter may be implemented in or across a plurality of processing chips or devices, and the storage may similarly be implemented across a plurality of devices. For example, these devices may include personal computers, network servers, handheld devices, and wearable devices.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Other examples of various processes that may be used with the exemplary method and system are described in the following documents: U.S. Pat. No. 9,289,150 entitled "Non-innovative Method and System for manipulating Cardiovasular Systems"; U.S. Pat. No. 9,655,536 entitled "Non-innovative Method and System for manipulating Cardiovasular Systems"; U.S. Pat. No. 9,968,275 entitled "Non-innovative Method and System for manipulating Cardiovasular Systems"; U.S. Pat. No. 8,923,958 entitled "System and Method for Evaluating an electrophoretic Signal"; U.S. Pat. No. 9,408,543 entitled "Non-innovative Method and System for manipulating Cardiovasular Systems and All-Cable monitor and Sudden Cardioc Death Risk"; U.S. Pat. No. 9,955,883 entitled "Non-innovative Method and System for manipulating Cardiovasular Systems and All-Cable monitor and Sudden Cardioc Death Risk"; U.S. Pat. No. 9,737,229 entitled "Noninrivative Electropathological Method for Estimating Mammarian Cardiac Chamber Size and Mechanical Function"; U.S. Pat. No. 10,039,468 entitled "Noninrivative Electropathological Method for Estimating Mammarian Cardiac Chamber Size and Mechanical Function"; U.S. Pat. No. 9,597,021 entitled "Noninrivative Method for Estimating Glucose, glycated Flemogolbin and Other Blood compositions"; U.S. Pat. No. 9,968,265 entitled "Method and System for manipulating Cardiovasular Systems From Single Channel Data"; U.S. Pat. No. 9,910,964 entitled "Methods and Systems Using chemical Analysis and Machine Learning to diagnosis" for the purposes of the present invention; U.S. patent application publication No. 2017/0119272 entitled "Method and Apparatus for Wide-Band Phase Gradient Signal Acquisition"; PCT publication No. WO2017/033164, International application for Wide-Band Phase Gradient Signal Acquisition, entitled "Method and Apparatus"; U.S. patent application publication No. 2018/0000371 entitled "Non-innovative Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation"; PCT publication No. WO2017/221221, International application entitled "Non-innovative Method and System for Measuring Myocardial Ischemia, Stenosis Identification, Localization and Fractional Flow Reserve Estimation"; U.S. Pat. No. 10,292,596 entitled "Method and System for Visualization of Heart Tissue at Risk"; U.S. patent application publication No. 2018/0249960 entitled "Method and System for Wireless Phase Gradient Signal Acquisition"; U.S. patent application publication No. 2019/0214137 entitled "Method and System to Assembly Disease Using Phase Space volume Objects" filed on 26.12.2018; international application No. PCT/IB/2018/060708 entitled "Method and System to Assembly Disease Using Phase Space volume Objects"; U.S. patent application publication No. US2019/0117164 entitled "Methods and Systems of De Noisying Magnetic-Field Based Sensor Data of Electrical Signals"; a U.S. patent application publication No. 2019/0214137 entitled "Method and System to Assembly Disease Using Phase Space Tomography and Machine Learning" filed on 26.12.2018; PCT International application No. PCT/IB2018/060709, entitled "Method and System to Assembly Disease Using Phase Space Tomography and Machine Learning"; U.S. patent application publication No. 2019/0384757 entitled "Methods and Systems to quantitative and removable anaerobic Noise in Biophysical Signals" filed on 18.6.2019; U.S. patent application publication No. 2019/0365265 entitled "Method and System to Assembly tension use Phase Space Tomography and Machine Learning"; U.S. patent application Ser. No./___________, entitled "Method and System to Assembly Disease Using Phase Space Tomography and Machine Learning", filed concurrently herewith (attorney docket No. 10321 and 034usl, and claiming priority from U.S. patent provisional applications Nos. 62/784,984 and 62/835,869); U.S. patent application Ser. No./________, entitled "Method and System To configuration and Use Neural Network To Association Medical Disease" (attorney docket No. 10321- > 037pvl and claiming priority To U.S. provisional patent application No. 62/784,925); U.S. patent application No. 15/653,433 entitled "discovery Novel Features to Use in Machine Learning Techniques, Such as Machine Learning Techniques for marking Medical Condition"; U.S. patent application No. 15/653,431 entitled "discovery genes to Use in Machine Learning Techniques"; U.S. patent application Ser. No./_ (priority of application with attorney docket No. 10321-041 pvl), entitled "Method and System to Association Disease use dynamic Analysis of Cardiac and photonic Signals"; application Ser. No./__ (priority of application having attorney docket No. 10321-040 pvl), U.S. patent application entitled "Method and System to Assembly diseases Using dynamic Analysis of biological Signals", each of which is incorporated herein by reference in its entirety.

Unless explicitly stated otherwise, it is not intended that any method set forth herein be construed as requiring that its steps be performed in a particular order. Thus, if a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that such order be inferred, in any respect. This applies to any possible non-explicit basis for interpretation, including: logic issues regarding step arrangements or operational flows; simple meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification.

While the methods and systems have been described in connection with certain embodiments and specific examples, it is not intended to limit the scope to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

The methods, systems, and processes described herein can be used to generate stenosis and FFR outputs for use with procedures such as placing a stent-graft within a vessel, such as an artery of a living (e.g., human) subject, as well as other interventional and surgical systems or processes. In one embodiment, the methods, systems, and processes described herein may be configured to use FFR/stenosis output to determine and/or modify the number of stents to be placed in a living body (e.g., a human) intra-operatively, including their optimal deployment location within a given vessel, and the like.

Examples of other biophysical signals that may be analyzed in whole or in part using the exemplary methods and systems include, but are not limited to: an Electrocardiogram (ECG) dataset, an electroencephalography (EEG) dataset, a gamma-synchronized signal dataset, a respiratory function signal dataset, a pulse oximetry signal dataset, a perfusion data signal dataset, a quasiperiodic biosignal dataset, a fetal ECG dataset, a blood pressure signal, a cardiac magnetic field dataset, and a heart rate signal dataset.

Exemplary analyses are useful for diagnosis and treatment of heart-related pathologies and disorders and/or nervous system-related pathologies and disorders, and such evaluations are applicable to diagnosis and treatment (including surgery, minimally invasive and/or drug therapy) of any pathology or disorder in which biophysical signals are related to any relevant system of a living body. One example of a cardiac aspect is the diagnosis of CAD and its treatment (alone or in combination) by various therapies, such as placement of stents in coronary arteries, performing atherectomy, angioplasty, prescription of medications and/or exercise prescriptions, nutritional and other lifestyle changes, and the like. Other heart-related pathologies or conditions that may be diagnosed include, for example, cardiac arrhythmias, congestive heart failure, valve failure, pulmonary hypertension (e.g., pulmonary hypertension due to left heart disease, pulmonary hypertension due to pulmonary disease, pulmonary hypertension due to chronic thrombosis, and pulmonary hypertension due to other diseases (e.g., blood or other diseases)), and other heart-related pathologies, conditions, and/or diseases. Non-limiting examples of diagnosable nerve-related diseases, pathologies or conditions include, for example, epilepsy, schizophrenia, Parkinson's disease, Alzheimer's disease (and all other forms of dementia), autism spectrum (including Alzheimer's syndrome), attention deficit hyperactivity disorder, Huntington's disease, muscular dystrophy, depression, bipolar disorder, brain/spinal cord tumors (malignant and benign), movement disorders, cognitive disorders, language disorders, various psychoses, brain/spinal cord/nerve injury, chronic traumatic encephalopathy, cluster headache, migraine, neuropathy (various forms including peripheral neuropathy), phantom limb/pain, chronic fatigue syndrome, acute and/or chronic pain (including back pain, back surgery failure syndrome, etc.), motor dysfunction, anxiety disorders, diseases caused by infection or extrinsic factors (such as Lyme disease, Huntington's disease, muscular dystrophy), Encephalitis, rabies), narcolepsy and other sleep disorders, post-traumatic stress disorder, neurological disorders/effects associated with stroke, aneurysm, hemorrhagic damage, and the like, tinnitus and other hearing related diseases/disorders, and vision related diseases/disorders.