Devices and methods for detecting and monitoring cardiovascular disease - Patents.com

Abstract

A device for cardiac monitoring, comprising a processor having a digital signal processing unit, the digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having one or more sensors, the processor including a program having executable instructions configured, when executed on the processor, to cause the processor to perform the following steps: synchronizing the processed signals of each sensor assembly, mapping the synchronized signals as waveforms of each sensor, calculating at least one cardiac function parameter as data values ​​with predetermined waveform amplitudes and predefined time intervals, and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine a cardiac condition.

Description

[Technical field]

[0001] The present invention relates to a device and method for cardiac monitoring, which may relate to a human or animal heart. More specifically, it is a method for calculating various parameters related to cardiac function, in particular to early detection of heart failure. The method can be used in combination with at least one sensor assembly, more preferably electrocardiogram electrodes arranged in one or more standard auscultation positions. [Background technology]

[0002] Heart failure (HF) is a condition that develops when a person's heart cannot pump enough blood for the body's needs. This can happen if the heart cannot fill with enough blood. It can also happen if the heart is too weak to pump properly. The term "heart failure" does not mean that the heart has stopped. However, heart failure is a serious condition that requires medical care.

[0003] There are an estimated 26 million people worldwide who have heart failure (HF). The known prevalence of HF is 1.5-2% of the adult population. Heart failure can develop suddenly (acute type) or over time (chronic type) as a person's heart weakens. It can affect one or both sides of the heart. Left-sided and right-sided heart failure can have different causes. In most cases, heart failure is caused by another medical condition that damages the heart. This can include coronary heart disease, carditis, high blood pressure, cardiomyopathy, or irregular heartbeat.

[0004] Heart failure may not cause symptoms right away. But eventually, you may feel fatigued or short of breath and notice fluid building up in your lower body, around your stomach, or in your neck. Heart failure can also damage your liver or kidneys. Other complications include pulmonary hypertension, or other cardiac conditions such as irregular heartbeat, valvular heart disease, and sudden cardiac arrest. Doctors usually classify a patient's heart failure according to the severity of their symptoms. The following list describes the New York Heart Association (NYHA) Cardiac Functional Classification, the most commonly used classification system. It places patients into one of four categories based on how limited they are during physical activity.

[0005] Patient Symptom Classification:I: No limitation of physical activity. Ordinary physical activity does not cause undue fatigue, palpitations, or dyspnea (shortness of breath);II: Slight limitation of physical activity. Comfortable at rest. Ordinary physical activity causes fatigue, palpitations, or dyspnea (shortness of breath);III: Severe limitation of physical activity. Comfortable at rest. Less than normal activity causes fatigue, palpitations, or dyspnea;IV: Unable to continue physical activity without discomfort. Symptoms of heart failure at rest. Discomfort increases if any physical activity is performed.

[0006] Objective assessment classification: A: No objective evidence of cardiovascular disease. No symptoms and limitations in usual physical activity; B: Objective evidence of minimal cardiovascular disease. Mild symptoms and slight limitations during usual activity. Comfortable at rest; C: Objective evidence of moderately severe cardiovascular disease. Significant limitations in activity due to symptoms, even during less than usual activity. Comfortable only at rest; D: Objective evidence of severe cardiovascular disease. Severe limitations. Symptoms experienced even at rest.

[0007] For example, a patient with minimal or no symptoms but a large pressure gradient across the aortic valve or severe obstruction of the left main coronary artery would be classified as functional capacity I, objective rating D. A patient with severe angina syndrome but angiographically normal coronary arteries would be classified as functional capacity IV, objective rating A.

[0008] Currently, heart failure is a serious condition with no cure. Typically, patients are symptomatic before treatment, which is often too late to have a significant effect on quality of life and cost of care. There is a clear need to detect HF early to allow early intervention to prevent or at least delay the onset of the disease. Treatment, such as healthy lifestyle changes, medicines, some devices and procedures, can help many people have a higher quality of life, especially if implemented early. It is important to note that the later the treatment (i.e., the higher the NYHA classification), the more costly it is, both in terms of the length of hospital stay, the amount of care required, and the cost of treatment, e.g., cardiac pacemakers, left ventricular assist devices, or even heart transplants.

[0009] While patients with more advanced disease can be monitored in clinical practice with pressure catheters in either the left or right heart chambers, the use of echocardiography is a mandatory test in patients with suspected heart failure. Echocardiogram - Ultrasound morphology; provides an assessment of heart chamber size and structure, ventricular function, valvular function, as well as important hemodynamic parameters. Echocardiograms may be performed before and after exercise, the latter to study cardiac function under stress at higher heart rates.

[0010] Additionally, jugular venous pulse testing, typically using ultrasound, is an important aspect of assessing a patient's volume status, especially in patients with cardiac, hepatic and renal failure. Elevated jugular venous pressure is a sign of abnormal right ventricular dynamics, most commonly reflecting elevated pulmonary capillary wedge pressure from left ventricular failure. This usually signifies fluid overload and indicates the need for diuresis.

[0011] Another parameter called heart rate variability (HRV) using ECG signals is a fundamental and non-invasive technique used to evaluate cardiac autonomic regulation. Traditional HRV has been shown to be significantly reduced in patients with HF, and this reduction is associated with the severity of HF and its prognosis.

[0012] The gold standard for continuous remote monitoring is an implantable arterial sensor that transmits blood pressure and heart rate. The cost of such devices and procedures can be as much as US$25,000, with associated risks of adverse events.

[0013] There is a long felt need for an externally applied device capable of measuring key biometrics related to cardiac function that are indicative of changes in the mechanical function of the heart in patients identified as being at risk for developing heart failure.

[0014] Any discussion of prior art throughout this specification should in no way be taken as an admission that such prior art is widely known or forms part of the common general knowledge in the art. Summary of the Invention [Problem to be solved by the invention]

[0015] It would be advantageous to provide an externally applied device that can measure important biometrics related to cardiac function that are indicative of changes in the mechanical function of the heart in patients identified as being at risk for developing heart failure.

[0016] There are also advantages to using the devices and systems in face-to-face clinical settings as well as virtual care / home settings.

[0017] Another advantage is that it allows for analysis of real-time data from remote medical consultations.

[0018] It would also be an advantage to provide a device that can provide similar information as larger capital equipment such as ultrasound (including echocardiograms), but which is portable, easy to use, and low cost.

[0019] It is advantageous for the device to derive information on important aspects of the cardiac cycle such as cardiac conduction time (when used in conjunction with an ECG), heart rate variability, valvular action, ejection time, refill time, contractility, cardiac elasticity, vascular compliance, hemodynamic function and ejection fraction, as well as changes in the jugular venous pulse which represents pressure in the right atrium.

[0020] The advantage is that it provides an easier assessment of cardiac function without the use of bulky equipment that requires specialist use, allowing more regular measurements to be taken as indicators of disease progression and the effectiveness of treatment.

[0021] It would also be advantageous to provide a device or sensor that measures important early signs of heart failure using a number of related biometrics including, but not limited to, changes in cardiac contractility or displacement, as well as central blood pressure and pulse transit time from the apex to the aortic arch or suprasternal notch.

[0022] It would also be advantageous to combine the sensor device with software that determines an early warning score for heart failure based on the signals received from the sensor or sensor dot.

[0023] It would also be an advantage to provide a method for estimating ejection and refill times from sensors as well as obtaining central and peripheral hemodynamic profiles of the body.

[0024] It is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art, or to provide a useful alternative. [Means for solving the problem]

[0025] A first aspect of the present invention may relate to a device for cardiac monitoring, comprising a processor having a digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having one or more sensors; the device including a program having executable instructions configured, when executed on the processor, to cause the processor to perform the following steps: synchronizing the processed signals of each sensor assembly; mapping the synchronized signals as waveforms of each sensor; rejecting signals having artifacts from the analysis process; calculating at least one cardiac function parameter as a data value by predetermining waveform amplitudes and predetermined time intervals; or combining several cardiac function parameters into an algorithm and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine a cardiac status.

[0026] Preferably, the sensor assembly comprises a force-sensing resistor, a displacement sensor, and a pair of electrocardiogram electrodes, the pair of electrocardiogram electrodes being embedded in the sensor assembly or attached using wires. Preferably, the program further comprises a non-transient memory configured to enable the processor to store a status data value corresponding to a first use of the at least one sensor assembly at the predetermined location; and to compare a second status data value corresponding to a second use of the at least one sensor assembly at the predetermined location, the processor being configured to determine a progression of the cardiac condition based on a difference in the status data values ​​from the first use and the second use. Preferably, the processor is further configured to provide a warning to the subject if the determined progression is worse compared to the previous use.

[0027] Preferably, the first use is pre-exercise and the second use may be post-exercise. Preferably, the first use is the time point used to compare values ​​from the second use after the system. The second use may be to evaluate disease progression over time, which may be days, or weeks, or months, and the evaluation may include the value of a specific treatment, which may be a drug, use of a medical device, supplements, dietary changes and / or exercise. The second use may also be immediately after the first use (e.g., within minutes of the first use), which may follow an exercise regime designed to put the cardiovascular system under stress, which may be from use on an exercise bike or treadmill. The compared values ​​may be used as a good predictive tool for the presence or progression of cardiovascular disease, which may not be limited to, for example, coronary artery disease. Since the amplitude is higher after exercise, it is important to analyze the increase after exercise from rest and monitor how long it takes for the amplitude to return to a normal size amplitude, as the amplitude is higher after exercise. One non-limiting example of normalizing the data may be to take a calculated time interval and divide by the calculated length of the cardiac parameter with another cardiac parameter. An example may be taking two cardiac function parameters such as an R wave versus another R wave in a cardiac cycle, with the time interval being between the time of the R wave and the time of the other R wave. It will be appreciated that the interval may be a P wave versus another P wave, or the interval may be a particular cardiac function parameter versus a different cardiac function parameter within a cardiac cycle.

[0028] Another aspect of the invention may relate to a device for cardiac monitoring, the device comprising a processor. A first sensor assembly including a first sensor group arranged at a predetermined location of a subject, the first sensor group configured to receive physiological signals. The first sensor group includes a pair of electrocardiogram electrodes in communication with a digital signal processing unit of the first sensor assembly to communicate the first physiological signals to the processor. The processor includes a program having executable instructions that, when executed on the processor, are configured to process the first physiological signals of a predetermined interval to obtain a representation as a data value; and determine at least one cardiac function or cardiac health indicator based on an analysis of the processed data values.

[0029] Preferably, the first group of sensors comprises a force sensing resistor and a displacement sensor.

[0030] Preferably, the processor is adapted to measure heart rate variability, contractility, and cardiac conduction time based on physiological signals received from the force sensing resistor, the displacement sensor, and the electrocardiogram electrodes when the sensor assembly is positioned on or near the subject's heart. An alternative is where the processor is adapted to measure pulse characteristics from the right atrium when the sensor assembly is positioned near the jugular vein in the patient's lower neck.

[0031] Preferably, the processor is configured to determine events from the cardiac cycle based on the received physiological signals, the determined events corresponding to at least one cardiac function selected from the following group: closure of the semilunar valves, ventricular blood refill period, cardiac conduction time, cardiac contractility, ejection period, cardiac output valve opening period, cardiac output valve closing period, and change in pressure in the right atrium when positioned on the jugular vein.

[0032] Preferably, the device further comprises a second sensor assembly including a second group of sensors, the second sensor assembly being located at a different predetermined location than the first sensor assembly.

[0033] Preferably, the first sensor assembly is positioned on the heart and the second sensor assembly is positioned at a peripheral location such as the arm, neck or leg where the processor is configured to derive the duration of the first and second heart sounds. More preferably, the processor is configured to derive the duration via low frequency components of signals detected by the sensor assemblies.

[0034] Preferably, the processor is configured to derive the pulse transit time.

[0035] Preferably, the processor is configured to derive the central blood pressure and vascular stiffness based on the relative timing between the opening and closing of the aortic valve and the waveform shape of the received physiological signal. More preferably, the processor is configured to derive the central blood pressure by using two sensors in the thorax. For example, a sensor may be placed in a first region of the thorax and another sensor may be placed in a second region of the thorax. Another example for deriving the central blood pressure may use a sensor placed in the thorax region and another sensor placed in the neck. Another example for deriving the central blood pressure may use a sensor placed in the thorax and another sensor placed in the iliac region.

[0036] Preferably, the processor is configured to derive force sensor amplitude differences over predetermined time intervals in the force signal to determine data values ​​of A) a calibrated peak amplitude of expansion and B) a calibrated peak amplitude of contraction, where only a single force sensor is positioned on the first region of the chest or where a first force sensor is positioned on the first region of the chest and a second force sensor is positioned on the second region of the chest.

[0037] Preferably, the processor is configured to derive the elasticity of the blood vessel based on the ratio of A:B.

[0038] Preferably, the processor is further configured to derive timing differences of received signals from the displacement sensor relative to the electrocardiogram electrodes to determine data values ​​of C) the time when the heart and / or blood vessels expand, and D) the time when the heart and / or blood vessels contract.

[0039] Preferably, the processor is configured to generate an indication of the subject's central hemodynamic function from the ratio of (A / C):(B / D) obtained from different predetermined locations of the sensor assembly. More preferably, the first and second sensors are positioned at a first and second location on the thorax, respectively.

[0040] Preferably, the processor is configured to synchronize physiological signals received from the first sensor assembly and the second sensor assembly at the two different locations.

[0041] Preferably, the processor is configured to determine the cardiac ejection fraction based on subtracting the physiological signals from the displacement sensors in the first sensor assembly and the second sensor assembly.

[0042] Preferably, the first sensor assembly is positioned above the apex of the heart and the second sensor assembly is positioned above the suprasternal notch, and the processor is configured to determine at least one cardiac function by a differential analysis step.

[0043] Preferably, the first sensor assembly is positioned over the apex of the subject's heart and the second sensor assembly is positioned at an aortic auscultation location of the subject, and the processor is configured to determine at least one cardiac function associated with the left chamber of the heart by the differential analysis step.

[0044] Preferably, the first sensor assembly is positioned over the subject's cardiac apex and the second sensor assembly is positioned at a pulmonary valve auscultation location of the subject, and the processor is configured to determine at least one cardiac function associated with the right chamber of the heart by the differential analysis step.

[0045] Preferably, the first sensor assembly and the second sensor assembly are positioned at adjacent locations on the top of the heart and the processor is configured to derive pulse elasticity and pulse timing, and then the processor determines arterial stiffness and blood pressure by a differential analysis step.

[0046] Preferably, the differential analysis step includes determining a state of the jugular venous pulse based on a physiological signal received from a first sensor assembly that correlates to various pressure changes in the right atrium when the sensor assembly is positioned on or near the jugular vein in the subject's neck.

[0047] Preferably, the processor further comprises, after the step of mapping the synchronized waveforms for each sensor, the step of annotating specific morphological features including potential artifacts.

[0048] Preferably, the processor further includes the steps of: calculating mean and variance measurements for each of the amplitudes and time intervals and removing identified artifacts; and joining selected signal portions to form an organized signal including the cardiac cycle and recalculating new mean and variance measurements after the steps of predetermining the waveform amplitudes and the predetermined time intervals.

[0049] Preferably, the processor further includes the steps of: calculating mean and variance measures for each of the amplitudes and normalized time intervals and removing identified artifacts; and joining selected signal portions to form an organized signal including the cardiac cycle and recalculating new mean and variance measures after the steps of predetermining the waveform amplitudes and the predetermined time intervals.

[0050] Preferably, the calculated time interval of the at least one cardiac function parameter is normalized to the length of the cardiac cycle.

[0051] Preferably, the device further comprises a non-transitory memory configured to store received physiological signals from the sensor assembly forming the historical data.

[0052] Preferably, the processor is configured to monitor the condition of the heart over time based on comparing derived data values ​​from most recent measurements with historical data values ​​when the sensor assembly is positioned at the same predetermined location.

[0053] Preferably, the program includes a set of predetermined parameters having data value thresholds for categories of cardiac function and cardiac health, and the processor is configured to compare the analyzed data values ​​with the set of parameters, thereby enabling the processor to identify an indication of cardiac function and cardiac health when the analyzed data values ​​are within the data value thresholds.

[0054] Another aspect of the invention may relate to a method for measuring specific cardiac functions comprising the steps of: a. using at least one sensor assembly, each sensor assembly being comprised of one or both of a force sensor and a displacement sensor; b. combining one or more sensor assemblies with at least a pair of electrocardiogram electrodes that are separate from the sensor assembly or included in the sensor assembly; c. placing the sensor assembly and electrocardiogram electrodes on the heart or on the outside of the chest adjacent to the heart at specific locations depending on the function to be measured; and d. recording the resulting signals and processing these signals to determine a value or range of values ​​equal to one or more specific cardiac function parameters.

[0055] Preferably, the processor calculates the relative timing of certain signal features, cardiac functions being contractility, cardiac conduction time, valve action, ejection time and refill time. Preferably, the relative timing between the opening and closing of the aortic valve, and their waveform shapes, correlate to central blood pressure and vascular stiffness. Preferably, the calibrated ejection time / refill time ratio correlates to the ejection fraction, typically measured with a cardiac ultrasound or other imaging device. Preferably, the force sensor and the piezoelectric sensor are calibrated, with one of the sensor assemblies positioned over the apex of the heart and the other positioned adjacent to the top of the heart. Preferably, the processor calculates the difference in amplitude of the force sensor at a particular point in the force signal corresponding to a calibrated peak amplitude of expansion (A) and a calibrated peak amplitude of contraction (B). Preferably, the ratio of the particular amplitude difference (A / B) is used to calculate the elasticity of the blood vessel. Preferably, the processor calculates the timing difference of a particular location of the signal from the piezoelectric sensor relative to the electrocardiogram, with these differences corresponding to vasodilation receiving a high pressure pulse (C) and vasoconstriction widening the pulse (D). Preferably, the ratio of the calculated peak amplitude (A) divided by the timing of diastole (C), and the ratio of the calculated peak amplitude (B) divided by the timing of systole (D), (A / C) / (B / D), are indicative of vascular compliance. Preferably, the processor calculates and compares the ratios (A / C) / (B / D) at different locations which gives an indication of the patient's central hemodynamic function.

[0056] Preferably, the processor calculates the heart rate and the resulting heart rate variability at each sensor location. Preferably, the processor subtracts the signals from the two calibrated piezoelectric sensors and then calculates the difference in amplitude of the subtracted signals at a particular point in the piezoelectric signal where the difference in amplitude correlates with the ejection fraction. Preferably, one sensor assembly is placed at the apex and one sensor assembly is placed at the suprasternal notch location to obtain a calculation of the overall cardiac function. Preferably, the overall cardiac function is at least one selected from the following group: cardiac contractility, central blood pressure, ejection fraction, timing of valve opening and closing, blood refill time, and blood ejection time. Preferably, one sensor assembly is placed at the apex and one sensor assembly is placed at the aortic auscultation location to obtain a calculation of the cardiac function related to the left chamber of the heart. Preferably, one sensor assembly is placed at the apex and one sensor assembly is placed at the pulmonary valve auscultation location to obtain a calculation of the cardiac function related to the right chamber of the heart. Preferably, the two sites are adjacent to the top of the heart and any other target arterial pulse locations (e.g. iliac crest, radial, etc...) and measurements of pulse elasticity as well as pulse timing are made to establish peripheral arterial stiffness and blood pressure. Preferably, the sensor is placed above the jugular vein to measure jugular venous pressure. Preferably, when multiple calculations are combined to form different sites, an indication of overall hemodynamic performance is given. Preferably, measurements are made simultaneously at three or more pulse sites. Preferably, the measurements are used to monitor the effect of blood pressure modifying drugs. Preferably, the obtained values ​​or changes in values ​​over time of the calculated cardiac function parameters are used to detect cardiac disease or progression of cardiac disease. Preferably, the method is performed using manual annotation using suitable software. Preferably, the method is performed automatically using a dedicated algorithm. Preferably, any artifacts are removed using manual processing. Preferably, any artifacts are removed using automatic processing. Preferably, the algorithms that calculate the specific parameters incorporate a methodology that allows for removal of artifacts from any of the signals. Preferably, the detection of cardiac disease or progression of cardiac disease is performed in either a clinical or virtual environment.

[0057] Another aspect of the invention is a device for cardiac monitoring, which may comprise a processor having a digital signal processing unit, which may be preferably assisted with a machine learning based AI processing unit, wherein the digital signal processing unit may be configured to receive and process physiological signals from at least one sensor assembly having one or more sensors, the sensors communicating or in conjunction with the AI ​​processing unit to learn and pre-process the signals to remove any undesired artifacts in the data; the processor, when executed on the processor, performs the following steps: synchronizing the processed signals of each sensor assembly; mapping the synchronization signal as a waveform for each sensor; performing AI-based smart template matching to score the similarity of all available cardiac cycles and find ideal portions of the signal to identify the signal portion associated with the desired cardiac cycle; annotating specific morphological features including potential artifacts; automatically splicing all selected signal portions to form an organized signal that includes all desired cardiac cycles; calculating at least one cardiac function parameter as data values ​​based on predetermined waveform amplitudes and time intervals; calculating mean and variance measures for each of the amplitudes and time intervals and removing identified artifacts; and replacing them with typical values; Recalculate new mean and variance measurements, then performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine the cardiac condition; The present invention may relate to a device that can include a program having executable instructions configured to perform the steps of the present invention.

[0058] Preferably, the heart may be a human heart or an animal heart.

[0059] Preferably, the difference analysis can take into account four major measures of variability such as range, interquartile range, measure of dispersion, and variance, so any one of them can be used. The range can be the difference between the maximum and minimum values, the interquartile range can be the range of the central half of the distribution, the measure of dispersion can be the average distance from the mean, and the variance can be the average of the squared distances from the mean.

[0060] In the context of the present invention, the words "comprise", "comprising" and the like are to be interpreted in their inclusive sense, i.e. "including but not limited to", as opposed to their exclusive sense.

[0061] The present invention should be interpreted with reference to at least one of the technical problems described or related to the background art. The present invention aims to solve or ameliorate at least one of the technical problems, which may result in one or more advantageous effects as defined herein and described in detail with reference to preferred embodiments of the present invention. [Brief description of the drawings]

[0062] [Figure 1] FIG. 1 is a diagram of a typical cardiac ultrasound procedure, where 101 is an ultrasound probe, 102 is a cone-shaped ultrasound beam, and 103 is a cross-section of the heart. [Diagram 2] A typical image (201) is shown in which the blood pathways (202), the aortic valve (203), and the atrioventricular valves (204) are identified. [Figure 3A] The timing of the ultrasound relative to the electrocardiogram (ECG) is shown, along with images of the ECG for various stages of the mechanical state of the heart. The ECG images are shown for P wave onset, when all valves are closed and the atria are refilling. [Figure 3B]The timing of the ultrasound relative to the electrocardiogram (ECG) is shown, along with images of the ECG for various stages of the mechanical state of the heart. An image of the ECG for the P wave body is shown, with the atria contracting and the valves to the ventricles opening. [Figure 3C] The timing of the ultrasound relative to the electrocardiogram (ECG) is shown, along with images of the ECG for various stages of the mechanical state of the heart. The ECG is shown for the end of the P wave, when the atria have completed their mechanical work and the valves are closed. [Figure 3D] Timing of ultrasound relative to the electrocardiogram (ECG) is shown. Also shown are images of the ECG for various stages of the mechanical state of the heart. Images of the ECG are shown relative to QRS onset, where the blood filled ventricles begin to depolarize. The relative atrium-ventricle pressure difference ensures that the valves seal (the first deflection peak on the dot indicates conduction time). [Figure 3E] The timing of the ultrasound relative to the electrocardiogram (ECG) is shown. Also shown are images of the ECG for various stages of the mechanical state of the heart. The ECG image is shown for the QRS body end, where the ventricles contract (contractility indicated by the large waveform and peaks on the dots). [Figure 3F] The timing of the ultrasound relative to the electrocardiogram (ECG) is shown, along with images of the ECG for various stages of the mechanical state of the heart. Images of the ECG are shown relative to QRS end / T wave onset, where the aortic valve opens and blood shifts into the aorta. [Figure 4]4 shows signals from a subject's heart using a device or sensor dot. Signal 409 is from an ECG, signal 410 is from a piezoelectric sensor, and signal 411 is from a force sensing resistor (FSR) sensor. 401-408 in FIG. 4 show quantitative timing from various pairs of different morphological features on the signals through the cardiac cycle. More specifically, 401 shows the atrial contraction period from the piezoelectric signal, which corresponds to the closure of the semilunar valves (aortic and pulmonary valves) while the mitral and tricuspid valves are open in the cardiac cycle. 402 shows the peak of the R wave of the ECG relative to the trough of the piezoelectric signal, which corresponds to the cardiac conduction time. 403 shows the ventricular isovolumic contraction period (all valves are closed) where the ventricles contract due to the QRS signal from the ECG. Cardiac contraction known as generated tension and shortening velocity. That is, the "strength" of the contraction of the myocardial fibers at a given preload and afterload 404 is the ejection period (405) from when the aortic and pulmonary valves open to when they close, indicating rapid ejection leading to relaxation due to the ECG t-wave. This is a measure of the time it takes to eject blood from the heart. 406 corresponds to the index time when the tricuspid and mitral valves open and blood begins to flow back into the ventricles. The time from 405 to 407 is called the isovolumic relaxation time, during which the heart relaxes with all valves closed. 408a corresponds to when there is rapid filling to the ventricles, leading to a period of slower filling (408b). For ease of understanding, location 1 is identified in two cardiac cycles indicating the corresponding refilling periods in each cycle. The rapid filling and slower passive filling of the ventricles generally results in 95% filling of the ventricles, with the remaining 5% being pushed in during atrial contraction (401). The cardiac cycle then repeats itself. [Diagram 5]The ECG shows the relationship between the sensor dots (on the chest and on the arm) and the chest signal is subtracted to get the actual acoustic signal. The blood pressure pulse is shown in the bottom image, from a Biopac gold standard device. The heart sound signal shows that the area corresponding to S1 corresponds to the closure of the mitral and tricuspid valves and corresponds to the pulse. Note that there is a delay in the pulse as it reaches the finger (bottom Biopac signal), indicating the 503 Pulse Transit Time (PTT). The second heart sound (S2) represents the closure of the semilunar valves (aortic and pulmonary valves). [Figure 6] Two sensor dots are shown, located at the apex 601 and suprasternal notch 602, which correspond to the proximity of two important auscultation locations above the fifth and second intercostal spaces. [Figure 7] Shown is the timing on the sensor dots for plotting the central hemodynamic profile. Period (1) corresponds to vasodilation receiving a high pressure pulse, and period (2) corresponds to vasoconstriction - the broadening of the pulse. (A) corresponds to the calibrated peak amplitude of dilation, and (B) corresponds to the calibrated peak amplitude of contraction. [Figure 8] 7 shows amplitudes of interest shown at 702-A and 702-B from piezoelectric waveforms when the displacement sensors of the first and second sensor assemblies are positioned above the apex and suprasternal notch of the heart, respectively, and a calculation of ejection fraction=B as a % of A. [Figure 9] 1 is a table showing how the program may calibrate certain parameters and timing of a list of cardiac functions.Heart Rate Variability (HRV) can be detected from an ECG and / or a Force Sensing Resistor (FSR) or a piezoelectric sensor. [Figure 10A] 13 shows a table indicating the sensor ID / type that is downloaded when a particular signal is downloaded / displayed. [Figure 10B] The sensor types corresponding to the sensor IDs in FIG. 10A are shown. [Figure 10C] 10C is a sensor legend for FIG. 10B. [Figure 11A] 1 shows various sensor locations on a patient's chest. [Figure 11B]1 shows various sensor locations on a patient's finger or wrist. [Figure 11C] 1 illustrates various sensor locations on a patient's neck or carotid artery. [Figure 11D] 1 illustrates various sensor locations on a patient's pelvic region. [Figure 11E] 1 shows various sensor locations on the femoral / popliteal / posterior tibial region of a patient. [Figure 12] 1 shows the synchronization of the ECG waveform, the chest piezoelectric waveform and the chest FSR waveform. The processor can have the amplified signal and perform time calibration to generate the resulting graph. [Figure 13] Shown are the magnified signals as waveforms and amplitude calibrations for FSR apex and FSR suprasternal notch locations. [Figure 14] 1 illustrates how a program can enable a processor to graph a parameter with respect to the time / date it was measured. In this particular example, left ventricular ejection time is used as an example of when a patient is receiving treatment. It will be appreciated that other parameters may also be graphed with respect to the date it was measured. [Figure 15] 1 illustrates an exemplary updateable care plan based on results from physiological measurements from a sensor assembly. [Figure 16] 1 illustrates predetermined time intervals and amplitudes that are measured to determine pulse transit time (PTT). In this particular example, PTT is measured from the apex to the suprasternal notch. The processor may be configured to derive or estimate central pressure based on these waveforms. It will be appreciated that similar processing can be used to determine PTT between other two locations. [Figure 17] It illustrates how a processor may estimate blood pressure based on calibration of the generated waveform, where peak-to-peak pressure can be obtained in a single point calibration and compared to beat-to-beat non-invasive blood pressure. [Figure 18]48 illustrates how a processor may determine different tissue compliances, in this example a chest waveform (top 4807) based on signals received from a front FSR and a back FSR from a sensor assembly versus a wrist waveform (bottom 4808) based on signals received by a front FSR and a back FSR from another sensor assembly. [Figure 19] A schematic diagram of a morphological band / sensor that can receive physiological signals from a region of the body, for example, the chest region, is shown. The signal is sent to a processor with a digital processing unit, and the program can be an artificial intelligence software where the processor can determine the risk or likelihood of heart failure (as shown as green / yellow / red on the representative monitor in the figure) when parameters such as those outlined in Table 2 are processed. The program can then generate a care plan that can recommend medicines, exercise and / or diet to proactively and proactively initiate efforts to reduce the risk as much as possible so as to work to avoid heart failure. The process can be scheduled at predefined date intervals so that the system and program can monitor the progress and / or monitor any deterioration of the heart function or parts. Also, quickly adapt the care plan to take into account the current measurements. This figure also shows a clinical flow diagram for monitoring a patient with heart failure. [Figure 20] 1 shows an overall system aimed at early heart failure detection. [Figure 21A] Shown are the carotid tracings, the first (S1) and second (S2) heart sounds, and the timing of the jugular venous pulse (JVP) displayed in relation to the electrocardiogram (ECG). [Figure 21B] Simultaneous carotid and venous pulse tracings taken with a Lombard's tambour in susceptibility mode are shown. x-x', relative positions of the writing points with the arc. The figure shows the method of marking the c-wave as well as the different nomenclature. The carotid and jugular venous pulse waveforms are shown. [Figure 21C](Top) shows four curves showing the transition from the right auricular pulse to the supraclavicular vein pulse: I - pressure change at the right auricular; II - pressure change in the intrathoracic vein; III - volume change in the neck vein; IV - supraclavicular pulse taken with a tambour. Figure 21C (bottom) shows the supraclavicular vein pulse (top) and the subclavian pulse (bottom). [Figure 22] 1 shows typical jugular vein pulses from an ultrasound probe before and after exercise. [Figure 23] The jugular vein pulse (JVP) is shown as recorded from both the sensor band and the sensor dot. [Figure 24] The signal from the neck band is shown when the subject is in various positions during the tilt table test, highlighting the change in signal as blood pressure changes with the tilt positions shown. [Figure 25A] Shown is an image of the screen of a computer-based analysis software system displaying sensory signals (e.g., piezoelectric DOT and ECG) that are automatically annotated for various morphological features that correspond to important cardiac parameters. The analysis software system automatically detects and removes artifacts in the signals before analytically annotating the remainder of the sensory signal to estimate a measure of variance of the important cardiac parameter. [Figure 25B] FIG. 1 shows an image of a screen of a computer-based analysis software system, displaying artifacts in the sensory signal [marked with an oval with an "X"] that need to be removed to make the signal more realistic prior to automatic annotation. Good segments of the sensory signal are marked with an oval with an "X". The system automatically eliminates artifacts in the signal prior to estimating the measure of variance. Removal of artifacts then reduces the measure of variance of the cardiac parametric calculations, thereby advantageously reducing the time over which cardiac parameters are calculated. [Figure 25C]1 shows an image of a screen of a computer-based analysis software system displaying the effect of artifacts on cardiac parametric calculations in real sensory signals. In the presence of signal noise and artifacts, the measurements of variance of important cardiac parameters, i.e. aortic volume shift, cardiac conduction time, ejection time, etc., are too high and erroneous. The system automatically eliminates the artifactual portion of the sensory signal before estimating the measurements of variance. Removal of artifacts reduces the measurements of variance of cardiac parametric calculations, which also advantageously reduces the time over which cardiac parameters are calculated. [Figure 25D] FIG. 1 shows an image of a screen of a computer-based analysis software system displaying the effect of artifacts on cardiac parametric calculations in a real sensory signal after automatic removal of significant artifacts. The system automatically eliminates artifactual portions in the signal before estimating the variance measurements. Removal of artifacts reduces the variance measurements of the cardiac parametric calculations. The variance measurements of important cardiac parameters, i.e. aortic volume shift, cardiac conduction time, ejection time, etc., are within clinically acceptable ranges, and therefore the sensory data and associated analytics are proven to be acceptable by clinicians. [Figure 25E] 1 shows an image of a screen of a computer-based analysis software system displaying the effect of artifacts on cardiac parametric calculations in real sensory signals after automatic removal of significant artifacts. Rapid template matching using machine learning is advantageous for automatically eliminating artifacts. This exemplary diagram shows a selection template as a gold standard respiratory cycle based on a piezoelectric sensor. This template is used to score each recorded respiratory cycle based on similarity. A higher similarity score represents a better match and thus indicates a good cycle, when the data can be clinically accepted. On the other hand, a lower similarity score indicates potential artifacts or other types of noisy signals and cycles. [Figure 25F]1 shows an image of a screen of a computer-based analysis software system, which displays an example of cardiac parametric calculations on real sensory signals, illustrating a methodology for automatically rejecting artifactual periods in recorded time series signals. Matching analysis based on machine learning or a machine learning engine includes advanced motif template matching, using the angular positions of multiple feature points within a time period defined by a respiratory cycle. This automatic visual approach mimics a cognitive interactive method to match and reject artifacts. The similarity score is the predicted outcome of a trained learning algorithm that automatically identifies artifactual periods that should be removed to tidy up the time series data. [Figure 25G] An image of a screen of a computer-based analysis software system is shown focusing on an area as a contour within a template 2598 of a piezoelectric-based respiratory cycle (as shown in FIG. 25E), which is also shown in box 2620 of the piezoelectric-based respiratory cycle (as shown in FIG. 25F with a similarity score=80%). FIG. 25G shows an automated machine learning algorithm that matches and finds all valid respiratory cycles present in the recorded time series data using a predefined threshold to identify an ideal match of a valid respiratory cycle recorded using a piezoelectric sensor. [Fig. 25H] 1 shows a schematic diagram of a methodology for rejecting artifacts in real sensory signals acquired by a sensor, illustrating a methodology for automatically rejecting artifactual periods in a recorded time series signal. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0063] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings and non-limiting examples. Embodiments of the present disclosure relate to sensing systems and methods for monitoring cardiac function and / or providing an indication of cardiac health based on processed physiological signals received from one or more sensor assemblies.

[0064] In a preferred embodiment of the present invention, the device 1000 may include at least one sensor assembly 1002, which may have a sensor group associated with each other. The sensor group may use a force sensor 1004, such as a force sensing resistor (FSR), and a displacement sensor 1006, such as a piezoelectric sensor. By using both sensors, the force displacement of the object, as well as the velocity of such displacement (from the piezoelectric sensor 1006) are measured simultaneously. The compressive and dynamic forces that may be exerted on the force sensor 1004 may be used to calibrate or adjust the displacement velocity signal generated by the displacement sensor 1006. This calibration may allow for an accurate and continuous direct measurement of the speed or velocity of the skin displacement as well as the displacement itself. Thus, an accurate and continuous measurement of the blood impulse, and therefore the heart impulse, may be obtained from the skin movement alone.

[0065] Combining the use of electrocardiogram (ECG) electrodes 1008 or leads 1008 in the sensor group can also be advantageous. ECG leads may be embedded in the sensor assembly. The ECG electrodes 1008 can receive signals of the frequency or rate of heart beats (leading to the calculation of heart rate variability), as well as the regularity or rhythm of the heart beats. Information from the ECG signals can provide the clinician with important information of the patient's heart, for example, regarding the contractility of the heart, possible stenosis of the coronary arteries, or irregular heart beats. The signals simultaneously generated by the sensor group synergistically and advantageously provide a more accurate identification or estimation of the ventricular blood ejection time / period as well as the blood refill time / period.

[0066] As shown in FIG. 1, 2D (two-dimensional) ultrasound can be used to verify the timing acquired by the sensor assembly 1002. FIG. 1 shows a diagram of a typical cardiac ultrasound procedure in which an ultrasound probe or transducer 101 is used to emit a cone-shaped ultrasound beam 102 or ultrasound pulse 102 into a tissue of interest, in this case the subject's heart. An image of a cross section of the heart 103 is shown as representing the tissue of interest. When the ultrasound pulse 102 encounters tissue, a portion of the pulse or wave is reflected back to the transducer 101, the percentage of the wave that returns depends on the density and size of the tissue being examined. This ultrasound procedure is used in clinical settings and is called an echocardiogram, which uses sound waves to produce an image of the heart. This procedure, or a common clinical test, allows a physician to see how the heart beats as well as how the heart functions, such as in the process of pumping blood. Images from an echocardiogram are used to identify heart disease. FIG. 2 shows a representative cardiac ultrasound image 201 in which blood passages 202 and other heart parts such as heart valves, e.g., aortic valve 203 and atrioventricular valves 204, can be identified. Ultrasound can be used, but ultrasound machines are expensive, and there is a long felt need to create a low-cost sensor assembly that a person can easily couple to their skin to obtain data using ultrasound, as well as to obtain additional physiological signals that can be analyzed and used to determine a subject's cardiac function and health. Ultrasound procedures can be a means of clinical validation for the sensor assemblies used.

[0067] 3A-3F show the timing of the ultrasound with respect to the electrocardiogram, with 301-306 showing images of the ECG for various stages of the mechanical state of the heart. FIG. 3A shows P-wave onset 301, with all valves closed 301a, 301b and the atria refilling with blood. FIG. 3B shows P-wave body 302, with the atria contracting 302a and the atrioventricular valves opening 302b. FIG. 3C shows P-wave end 303, with the atria completing contraction 303a and the atrioventricular valves closing 303b. FIG. 3D shows QRS onset 304, with the ventricles refilled with blood beginning to depolarize and the relative pressures within the atria-ventricles ensuring a seal of the valves 304b. This can be seen by the first deflection peak on the dot indicating the conduction time. FIG. 3E shows QRS body end 305, indicating complete ventricular contraction, with contractility indicated by the large waveform and peak on the dot 305c. FIG. 3F shows the QRS end or T wave beginning 306, with the aortic valve 306a shown opening as blood moves or shifts into the aorta of the heart.

[0068] As shown in FIG. 21A , a graph 2100 illustrates various morphological characteristics of the carotid artery pulse 2102 and the jugular vein pulse 2104 (A, X, C, X, V, and Y) relative to the S1 and S2 heart sounds 2106 and the P, QRS, and T waves in an ECG or EKG 2108: A wave is right atrial contraction; The C wave is the initial ventricular contraction; X descent (parts 1 and 2) is the downward movement of the ventricle during systolic contraction; V wave is right atrial filling; Y descent is the opening of the tricuspid valve during diastole. The timing and amplitude of these morphological changes can change when there are pressure changes within the right atrium.

[0069] FIG. 21B shows a graph 2150 with simultaneous carotid pulse tracing 2152 and venous pulse tracing 2154 taken with a Lombard's tambour in susceptibility form. x-x', relative position of the writing point with the arc. This figure shows how to mark the c-wave as well as different nomenclature. The carotid pulse waveform and the jugular venous pulse waveform are shown.

[0070] As shown in the top diagram of Fig. 21C, the graph 2160 shows four curves showing the transition from the right auricular pulse to the supraclavicular vein pulse: I - pressure change at the right auricular 2162; II - pressure change at the intrathoracic or extrathoracic venous pressure 2164; III - volume change at the neck vein 2166; IV - supraclavicular pulse taken at the tambour 2168. As shown in the bottom diagram of Fig. 21C, the supraclavicular vein pulse 2170 (top) and the subclavian pulse 2172 (bottom) are shown.

[0071] Figure 22 shows jugular vein pulses from an ultrasound probe. The first image shows a baseline JVP 2200 (left image) highlighting a morphology similar to the typical JVP shown in Figure 3H. The right image shows a JVP 2250 with motion, showing a change in morphology with increasing amplitude.

[0072] 23 shows a JVP signal 2300 recorded from a neck band sensor 2306 and a neck dot sensor 2308. Both show the typical JVP morphology outlined in textbooks, allowing identification of the A wave corresponding to right atrial contraction, the C wave corresponding to early ventricular contraction, the X descent (parts 1 and 2) corresponding to downward movement of the ventricles during systolic contraction, as well as the V wave corresponding to right atrium filling, and the Y descent corresponding to tricuspid valve opening during diastole.

[0073] FIG. 24 shows schematic diagrams 2400, 2420 illustrating the change in JVP pulse detected using the neck band at various table tilt angles 2402, 2404, 2406, 2422, highlighting the value of the sensor for detecting JVP at different blood pressures.

[0074] The ultrasound can determine the timing and the sensor assemblies 1002 can be used to measure heart valve signals and determine the time or duration of blood refill or blood ejection from one or more of the sensor assemblies 1002. The sensor assemblies 1002 can be worn or placed on the skin over a predetermined location of interest, such as one of the sensor assemblies can be over the heart, for example as shown in FIG. 4, to allow continuous and non-invasive monitoring of the mechanical events of each cardiac cycle of a person or subject. While one sensor assembly 1002 can be used to obtain useful information regarding physiological parameters such as the identification and duration of each phase of the cardiac cycle 401-408, further information regarding physiological parameters can be obtained when a second sensor assembly is coupled to the subject's skin at a different predetermined location than the first sensor assembly 1002 to determine the period during which the heart valves are open and closed, cardiac contractility, stroke volume, cardiac output, pulse transit time, and central arterial pressure.

[0075] It will be understood that the sensor assembly 1002 and its sensors (1004, 1006, 1008) communicate with the processor 1100 by means of a memory or program 1102, which may enable the processor to execute instructions and store received physiological signals 1004a / 1006a / 1008a acquired by the sensors 1004 / 1006 / 1008, respectively. The processor may have a digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having one or more sensors. The processor 1100 may process the received signals 1004a / 1006a / 1008a as data values ​​1004b / 1006b / 1008b, which may be specifically selected to calculate a particular cardiac function and / or may be used to provide an indication of cardiac health based on the analyzed data values. The program or software 1102 of the processor 1100 may be configured to store the measured physiological signals 1004a / 1006a / 1008a, which may be processed and represented as data values ​​1004b / 1006b / 1008b. The stored information may relate to a given position 1004c / 1006c / 1008c of the sensor assembly 1002, the received signal, a time or period or a record 1004d / 1006d / 1008d. It will be understood that the signals are received at the same time and thus the signals are synchronized. The stored data 1104 may be assigned as current data 1106, which may be the latest measurement value or historical data 1108. A program or software 1102 having specific algorithms 1110 for identifying relevant predefined intervals of the received signal 1004a / 1006a / 1008a for a particular measurement, as well as for deriving / performing differential analysis or calculating and / or calibrating the signal to generate data values ​​1004b / 1006b / 1008b. The data values ​​1004b / 1006b / 1008b can be used to compare and match a set of relevant specific parameters of the cardiovascular system shown in Table 2, which can convey physiological risk of cardiac condition or health.Physiological risk for a particular parameter set may have a value range threshold, which may have a data value range indicative of healthy function and may have a data value range indicative of a person seeing a clinician if this predetermined healthy range is exceeded. The advantage of storing historical data 1108 is that this data 1108 may be compared to current data 1106 which may provide an indication of cardiac health over time. That is, if analysis of the data over time is in a worsening state, the measurements may be within a healthy range, but if the processor predicts or predicts that there may be a problem or heart failure, the system may also provide an indication to the person or provide an alarm to alert the person to see a clinician or seek further medical advice.

[0076] As shown in FIG. 4, ejection and refill times 404, 408a and 408b with other intervals / points can be identified by the ECG electrodes 1008 with displayed waveform 409, the chest force sensor 1004 with displayed waveform 411, and the displacement sensor 1006 with displayed waveform 410. Vertical lines across the signal for a given period, now displayed as waveforms (409, 410, 411) from each sensor (1004, 1006, 1008), indicate the identified cardiac functions 401-408a and 408b and what is occurring during that particular time period. More specifically, 401-408 show the quantitative timing from various pairs of different morphological features on the signal through the cardiac cycle. 401 indicates the atrial contraction time from the piezoelectric sensor signal corresponding to when in the cardiac cycle the atria contract due to the P wave from the ECG, 402 indicates the peak of the R wave of the ECG relative to the trough of the piezoelectric signal corresponding to the cardiac conduction time, and 403 indicates the period during which a filled ventricle contracts at a constant volume, with all valves closed 1207 by the myocardial fibers at a given preload and afterload, and the semilunar valves (aortic and pulmonary) are closed. Cardiac isovolumic contraction 1207 may be known as the tension generated and the shortening velocity (i.e., "strength") of the contraction. The period indicated by 404 is identified as the ejection period, which may be the time it takes to eject blood from the heart leading to the closure of the aortic and pulmonary valves at 405. The period indicated by 407 is identified as the time the aortic and pulmonary valves are closed, and the time the tricuspid and mitral valves are open (i.e., all valves are closed) at 406. This is called the isovolumic relaxation time. When the AV valves (mitral and tricuspid) open at 406, blood begins to refill the ventricles, and 408a and 408b indicate the time it takes to refill the ventricles with blood before an atrial contraction completes filling of the ventricles with enough pressure generated to close the AV valves. The cardiac cycle then repeats itself.

[0077] From the use of two or more sensor assemblies, more physiological signals can be collected. As shown in FIG. 5, this shows the relationship between the ECG, a first sensor assembly 1002 on the chest, and another sensor assembly 1002 on the arm. The chest signals acquired by these sensor assemblies 1002 can be differential 1200 by the processor 1100 to obtain an actual acoustic signal 1202. The processor 1100 can be configured to determine the subject's blood pressure pulse, as shown in the Biopac signal or waveform 1204. The heart sound signal 1202 can show an area 503 corresponding to the first heart sound or S1 501, which can correspond to the mitral and tricuspid valves closing, corresponding to the pulse. Also, the second heart sound or S2 502 can represent the closing of the semilunar valves (aortic and pulmonary valves). Since the pulse delay is known when it reaches the finger in FIG. 5 (Biopac signal 1204 in the bottom diagram), the program 1102 of the processor 1100 can factor in this natural time delay and derive the pulse transit time (PTT) 503 based on these received synchronized physiological signals 1004a / 1006a / 1008a.

[0078] In another preferred embodiment, the processor 1100 may be configured to determine the health of the aortic and pulmonary valves from the received physiological signals 1004a / 1006a / 1008a obtained from placing the first sensor assembly 1002 to the right of the suprasternal notch and the second sensor assembly 1002 to the left of the suprasternal notch. As shown in FIG. 6, an example of two sensor assemblies 1002 are placed on the subject, and in this particular example, the first sensor assembly or first sensor dot may be placed on the apex 601 and the second sensor assembly or second sensor dot may be placed on the suprasternal notch 602. These two locations may correspond to the proximity of two important auscultation locations, the fifth intercostal space and above the second intercostal space.

[0079] The received synchronized physiological signals 1004a / 1006a / 1008a from the sensors 1004, 1006, 1008 allow the processor 1100 to derive or measure pressure wave amplitudes at predetermined time intervals such that a first sensor assembly 1002 may be on the heart and a second sensor assembly 1002 may be at a different location in the body, and data values ​​related to rise / fall times can give an indication of elasticity of the output or aortic vessel of the subject. Information or data from evaluating the relative timing relationship between the aortic vessel and other arteries in the body such as, but not limited to, the iliac crest arteries, the carotid arteries (neck pulse), the radial arteries (radial pulse), etc. Examples of placement of the sensor assemblies can be shown in Figures 11A-11E. A second or more sensors 1002 may be positioned together at any given location on the body so that the received physiological signals 1004a / 1006a / 1008a from each of the sensor assemblies 1002 can be used to plot a complete hemodynamic diagram of a human.

[0080] As shown in Figures 7 and 8, the processor 1100 can derive specific time intervals 1300, 1302 from the received physiological signals of each of the sensor assemblies to plot the central hemodynamic profile of the subject. In particular, the time intervals shown in C1300 can correspond to the cardiac function of vasodilation, where at the predetermined time intervals the blood vessels may receive high pressure pulses. The time intervals shown in D1302 can correspond to the cardiac function of vasoconstriction, where at the predetermined time intervals the pulses may be broadened or there may be a broadening of the pulses. For a chart in which a first sensor assembly may be placed over the apex and a second sensor assembly may be placed over the suprasternal notch, the processor 1100 can derive or calibrate the peak amplitude A1304 for diastole and the peak amplitude B1306 for systole at these specific predetermined time intervals. 8 may show how the processor 1100 determines the ejection fraction 1308 from deriving or calibrating the amplitudes of 702-A and 702-B from the piezoelectric waveforms when the displacement sensors 1006 of the first sensor assembly 1002 and the second sensor assembly 1002 are positioned on or near the apex and on the suprasternal notch 602 of the heart 601, respectively. The processor 1100 may execute the following formula: ejection fraction=B as a % of A.

[0081] With further information by deriving values ​​of A and B from the piezoelectric waveform 410, the elasticity of the blood vessel can be determined. For example, the value from the ratio of A:B can represent the elasticity of the blood vessel, and the value from the ratio: (A / C):(B / D) can be an indicator of the blood vessel compliance. By comparing the values ​​from the ratio of (A / C):(B / D) at different locations, a hemodynamic diagram of the human body can be plotted. The derived values ​​of the ratio of the calibrated blood ejection time or the calibrated blood refill time can be compared to the ejection fraction measured with ultrasound or other imaging devices used in clinical settings to be accurate and functional. The amplitude ratio of the subtracted calibrated piezoelectric waveform or displacement signal placed over the apex of the heart 601 and placed over the suprasternal notch 602 can be correlated with the ejection fraction 1308.

[0082] It will be appreciated that the program 1102 or software 1102 of the processor 1100 may use a dedicated algorithm 1100 that can synchronize the received physiological signals from each sensor assembly in use and depending on where the sensor assemblies are placed on the body, the program may store the received physiological signals so that a person can track the progression of their cardiac health, which may be essential for early detection of cardiac disease or the progression of cardiac disease.

[0083] Although a device or apparatus as described may be used, it will be understood that this is also a method or system for monitoring cardiac health. It will be understood that a processor 1100 including a program 1102 is required for the method steps to derive data values ​​1004b / 1006b / 1008b from received physiological signals 1004a / 1006a / 1008a from sensors in a sensor assembly 1004 / 1006 / 1008, as well as software 1102 and algorithms 1110 that enable the determination of cardiac function and / or an indication of cardiac health.

[0084] For example, for a measurement procedure for determining cardiac contractility 1207 and estimating central blood pressure 1208, the device may require at least two sensor assemblies 1002 or two sensor dots 1002 and one or more ECG electrodes 1008 (one-lead ECG), where a first sensor assembly 1002 may be placed over the apex 601 and a second sensor assembly may be placed over the suprasternal notch 602. The processor 1100 may be configured to measure the following parameters: pulse transit time (PTT) 503 or PAT, where the PAT parameter is estimated as the time difference between the R peak of the ECG and a point on the PPG rising edge. The processor 1100 may be configured to calibrate the received physiological signal from the sensor assembly 1002, where the processor 1100 may derive or subtract 1200 the piezoelectric waveform 410 to determine or estimate the ejection fraction 1308. Additionally, evaluation of the derived values ​​and the timing between the opening of the valves may enable the processor 1100 to derive or calculate the blood refill times 408 a and 408 b and the blood ejection time 404 .

[0085] For example, the processor 1100 may be configured to accurately estimate the ejection fraction 1308 by using two sensor assemblies 1002 with one lead ECG 1008 that can be added for better timing measurements. The ECG lead 1008 may be formed of an ECG electrode 1008 embedded in the sensor assembly 1002 or a sensor dot 1002. The sensor dot 1002 may be placed at the apex 601 and aortic auscultation location 603. Similarly, the parameters to be measured are PTT 503, estimated ejection fraction 1308, and assessment of timing between opening of valve 1310, as well as derivation or calculation of blood refill time 408a and 408b and blood ejection time 404. By placing the sensor assembly 1002 over the apex 601 and aortic auscultation location 603, the parameters are more accurate as they target the left heart. Similarly, the same useful measurements can be made on the right heart by moving the second sensor assembly from the aortic auscultation position 603 to the pulmonary valve auscultation position 604.

[0086] The sensor assembly 1002 can be moved from above the apex 601 to any other target arterial pulse location such as the iliac crest artery, radial artery, etc., so that the central hemodynamic diagram can be determined. Measurements of pulse elasticity as well as PTT 503 or PAT can be derived from the received physiological signals 1004a / 1006a / 1008a of the sensor assembly 1002 to establish peripheral arterial stiffness 1206 and blood pressure 1208. The measurements can be improved if the ECG signal 1008a is combined with the force sensor 1004 and displacement sensor 1006 in the sensor assembly 1002. The measurements can be further improved if the sensor assembly 1002 or sensor dot 1002 is placed at each peripheral pulse that it may be intended to monitor. This allows for simultaneous beat-to-beat monitoring. Monitoring cardiac health and hemodynamic diagrams can be essential and important when a person may be receiving blood pressure modifying medication, such as when the person may be in an intensive care unit (ICU).

[0087] In another embodiment of the present invention, the processor 1100 is an embedded system 1101 for receiving physiological signals 1103 from the sensor assembly 1002 or sensor dot 1002. The processor 1100 may comprise a field programmable gate array (FPGA) unit 1105 for configuring the processor 1100 to perform the functions described above. The embedded system 1101 may have its own display 1107 or touch screen 1107 or an interface 1107 for connecting to an external display 1109 or touch screen 1109 to present the graphs or waveforms 409, 410, 411 as shown in Figures 4, 5, 7 and 8. The display 1109 and touch screen 1109 may also display the indicators of early detection of heart failure generated by the processor 1100 described above. The embedded system 1101 may also provide a wired or wireless interface 1111 for connecting the sensor assembly 1002 or sensor dot 1002. In one embodiment, the processor 1100 is adapted to generate a trigger signal 1113 for the sensor 1002 or sensor dot 1002 at each time interval 1115. When the sensor 1002 or sensor dot 1002 receives such a trigger signal 1113, the sensor 1002 or sensor dot 1002 performs a measurement of physiological data of the subject. In one embodiment, the embedded system 1113 provides a buffer memory 1117 for each sensor or sensor dot interface. The embedded system 1113 can be associated with a server device 1119 for further processing the data 1004a / 1006a / 1008a received from the sensor or sensor dot 1002. In one embodiment, the system of the present embodiment includes a Health Level Seven or HL7 protocol stack for formatting the received physiological data before forwarding to the server device 1119.

[0088] In another embodiment, the processor 1100 is a computer or smart device 1100, and the method for generating an indicator of early detection of heart failure generated by the processor described above is implemented as a software application 11002. The method utilizes a central processing to control one or more sets of sensors or sensor dots 1002 to measure physiological signals of a subject up to 1121. In one embodiment, the method can select different sets of sensors or sensor dots 1002 for differential analysis measurement 1200. The method is adapted to trigger the measurement of the sensors or sensor dots at different time intervals and collect physiological signals from the sensors or sensor dots 1002 of the present invention to generate an indicator of early detection of heart failure to alert a health professional. The computer or smart device 1100 can connect the sensors or sensor dots 1002 via a wired or wireless interface 1123. In one implementation, the sensor or sensor dot 1002 is an embedded device 1113 equipped with a communication unit 1125 for communicating via a wireless protocol 1127 such as Bluetooth 1129, WIFI 1131, Ethernet 1133, 5G 1135, etc.

[0089] In one embodiment, the software 1102 is adapted to learn signal patterns 1137 from the historical data 1108 to improve the timing for triggering measurement signals 1139 for different sets of sensors 1002 or sensor dots 1002. The software 1102 can learn and recognize the patterns 1137 and derive 1139 measurement timing intervals 1141 via artificial intelligence algorithms 1143. The software 1102 or program 1102 can also synchronize the received data 1145 from the sensors as a graph or waveform presented based on time stamps in the data. This information can enable the processor 1100 to derive times and / or amplitudes corresponding to particular cardiac functions or events, which can be used to calculate cardiac functions such as blood refill times 408a and 408b and blood ejection time 404, vasoconstriction 1207 and vasodilation 1209.

[0090] In another embodiment of the present invention, there is provided a method for generating an indicator of early detection of heart failure, the method comprising the steps of: Step 1: Select the sensor type, location and configuration (shown in Figures 10A and 10B, 10C and Table 1). As shown in the table of FIG. 10A 1400, the identification of the sensor group is attributed to a sensor ID 1402. When a signal is acquired, it can be attributed to a particular sensor ID / type depending on the signal sensed by one or more sensor assemblies. As shown in FIG. 10B and diagram legend 10C, each of the sensor assemblies (sensors 1-10) 1002 can be selected. For sensor assemblies 6-10, ECG electrodes 1008 are shown embedded in the sensor assembly. It will be understood that sensor assemblies 2-5, 7-10 can have front FSR 1004 and back FSR 1004 components. For example, in one embodiment: If signals from the force sensor 1404 and the displacement sensor 1406 are detected / transmitted, a sensor ID number of 1 is attributed. If signals are detected / transmitted from the first force sensor 1404, the displacement sensor 1406, and the second force sensor 1408 that can calibrate the first force sensor 1404, then a sensor ID number of 2 is ascribed. If signals are detected / transmitted from the first force sensor 1404, the displacement sensor 1406, the second force sensor 1408 capable of calibrating the first force sensor 1404, and the disconnected FSR 1410, sensor ID number 3 is attributed. If signals are detected / transmitted from the first force sensor 1404, the displacement sensor 1406, the second force sensor 1408 which can calibrate the first force sensor 1404, the disconnected FSR 1410, and the first respiratory band 1412, sensor ID number 4 is attributed. When signals are detected / downloaded from the first force sensor 1404, the displacement sensor 1406, the second force sensor 1408 which can calibrate the first force sensor 1404, the disconnected FSR 1410, and the first and second respiratory bands 1412, 1414, sensor ID number 5 is attributed. If signals from the first force sensor 1404, the displacement sensor 1406, and the ECG electrode / 1-lead ECG 1416 are detected / transmitted, then sensor ID number 6 is attributed. If a signal is detected / transmitted from the first force sensor 1404, the displacement sensor 1406, and the ECG electrodes / 1-lead ECG 1416, the second force sensor 1408 capable of calibrating the first force sensor 1404 is attributed sensor ID number 7. If signals are detected / transmitted from the first force sensor 1404, the displacement sensor 1406, and the ECG electrodes / 1-lead ECG 1416, the second force sensor 1408 which can calibrate the first force sensor 1404, and the disconnected FSR 1410, sensor ID number 8 is attributed. If signals are detected / transmitted from the first force sensor 1404, displacement sensor 1406, and ECG electrodes / 1-lead ECG 1416, a second force sensor 1408 capable of calibrating the first force sensor 1404, a disconnected FSR 1410, and a first respiratory band 1412, sensor ID number 9 is attributed. When signals are detected / transmitted from the first force sensor 1404, the displacement sensor 1406, and the ECG electrodes / 1-lead ECG 1416, the second force sensor 1408 which may calibrate the first force sensor 1404, the disconnected FSR 1410, and the first and second respiratory bands 1412, 1414, a sensor ID number of 10 is attributed. It will be appreciated that the "disconnected" in the term disconnected FSR may be defined as not being mechanically connected to the front sensor - "mechanically isolated" - such that the signal from the disconnected sensor comes only from the fingers / wrist and not the chest. Step 2: Select the set of biometrics to be measured As shown in FIGS. 11A-11E, locations for the various sensor assembly positions can be located across the following areas of the body: As shown in FIG. 11A, the following chest locations can be selected for placement of one or more sensor assemblies 1002: apex (position T) 601, suprasternal notch (position S) 602, aortic valve (position A) 606, pulmonary valve (position P) 607, mitral valve (position M). As shown in FIG. 11B, the following hand and wrist locations can be selected for placement of one or more sensor assemblies: wrist (position R) 609, and fingers (position F) 610; As shown in FIG. 11C, the following neck locations can be selected for placement of one or more sensor assemblies: carotid artery (location C) 611; As shown in FIG. 11D, the following pelvic locations can be selected for placement of one or more sensor assemblies: iliac crest (location I) 612; As shown in FIG. 11E, the following femur to ankle locations can be selected for placement of one or more sensor assemblies: femur or groin (location F) 613, popliteal fossa or back of knee (location P) 614, posterior tibia or ankle (location PT) 615. In one embodiment, physiological signals sensed at the enumerated locations or positions may provide central and peripheral hemodynamic profiles of the human body. It will be appreciated that sensors may also be placed on other parts of the body not listed so that more signals or information may be received and analyzed to obtain a more comprehensive hemodynamic picture of the human body. Table 1 shows the minimum number of sensors required, as well as the sensors required to acquire a particular biometric or subject. [Table 1-1] [Table 1-2] [Table 1-3] [Table 1-4] The minimum locations required for a column in Table 1 may be apex, suprasternal notch (SN or SSN), aortic valve (AV), pulmonary valve (PV), radial, iliac, digital, carotid. In one embodiment, peripheral hemodynamics may be measured from the heart to the arteries of the legs (such as femoral, popliteal, or tibial). Additionally, vascular hemodynamics may be measured between any two arterial locations. Step 3: Synchronously receiving signal data or raw signal data from the sensor assemblies such that all waveforms are acquired simultaneously and the waveforms 409, 410, 411 are aligned in time (as also shown in FIG. 12). Step 4: Calibrate the amplitude so that the amplitude of the waveform can be derived (as shown in Figure 13) Step 5: The processor receives the data and pre-processes the data by scaling, noise filtering, normalization, etc. The program may be configured to map a waveform associated with the signal received from a particular sensor in the sensor assembly. Step 6: Once the signal is converted or digitized, it is uploaded to a cloud or server and downloaded in real time by a processor, which is then configured to select and display data from the cloud in real time. The data can be data from a current measurement or a past measurement (see Table 2 below). Table 2 shows exemplary baseline thresholds for parameters according to a system capable of determining a patient's risk of heart failure. [Table 2] From Table 2, in one implementation, a questionnaire regarding uncontrollable risk factors may be entered by the person or the person's clinician. In another embodiment, the risk factors are generated within the processor. In another embodiment, the processor may retrieve the list from a server and personalize it with preliminary signals obtained from the sensor. Such uncontrollable risk factors may assist the clinician to flag / note those who are at higher risk or have a disposition to have heart failure. Based on the resulting generated risk value, care and monitoring frequency may be generated and recommended / required for those categorized as high risk. Uncontrollable risk factors may include family history of heart failure, gender, age, family history of cardiomyopathy, any history of rheumatic fever, any history of alcohol abuse, any history of drugs or medications that may damage the heart muscle (e.g., some cancer drugs). Controllable risk factors are primarily parameters related to a person's lifestyle and / or diet. Such controllable factors can be smoking, heavy drinking (number of times drinking more than 3 drinks per hour per week), diet (rating out of 10 based on variety, balance, and health), exercise time, and weight (BMI range). As possible indicators of heart failure, a patient may have any of the following symptoms: shortness of breath during activity or when lying down, fatigue and weakness, swelling in the legs, ankles and feet, rapid or irregular heartbeat, decreased ability to exercise, persistent cough or wheezing with white or pink bloody mucus, swelling around the stomach (abdominal area), nausea and loss of appetite, difficulty concentrating or decreased alertness, chest pain if heart failure is caused by a heart attack. If any of the above symptoms are "yes," the program can categorize the patient as "red." Risk factors from monitoring from the patient managing the use of the device can determine the following parameters of interest: resting heart rate, heart rate variability (ms), respiratory rate, dyspnea (shortness of breath) with exercise, blood pressure (systolic), very rapid weight gain (kg) from fluid accumulation over a short period of time, e.g. one month. Heart failure test metrics from a clinician administering the use of the device of the present invention can determine the following parameters of interest: heart sounds (e.g., third sound), lung sounds (wheezing or crackles), pulse transit time from base to saccades, cardiac conduction time, valvular action, ejection time, refill time, contractility, elasticity, vascular compliance, and hemodynamic function. In response to the questionnaire in one embodiment, the algorithm of the program can categorize the risk value into three or more parameter groups or sets, for example, green, yellow, and red. Green is low risk of heart failure, yellow is medium risk, and red is high risk. It will be understood that these thresholds may have intermediate parameters close to the threshold boundaries, which may help indicate higher or lower medium risk. In another embodiment, the risk value may be a continuous value, or an equation for further calculation. It will be understood that if at least one uncontrolled risk factor is red, then a person should be monitored according to the high-risk heart failure protocol, regardless of whether other factors are green or yellow. Step 7: Comparing with the example signal / reference signal. A marked waveform is shown in Figure 4, with lines indicating certain morphological features throughout a person's cardiac cycle. Also, the example signal / reference signal may have the same or similar waveform as Figure 4 (not shown). Step 8: Measuring the test signal based on the example signal / reference signal. An example of the test signal can be shown in Figure 4, and the processor can search for an example signal / reference signal with a similar or exact waveform and a predefined time interval as in Figure 4 to perform the measurement. Step 9: Calculate the values ​​of the key metrics based on the calibration. The values ​​in the table change automatically depending on the space interval for the calibration. Example values ​​are shown in FIG. Step 10: Mapping or graphing the parameter over time. For example, a graph 1500 of left ventricular ejection time is shown in FIG. Step 11: Preparing a care plan 1600 that can be updated in real-time based on measurements / signals received from one or more sensor assemblies 1002. For example, the customized care plan 1600 may be summarized in a table such as that shown in FIG. Step 12a: Differentiating the displacement sensor signal / piezoelectric sensor signal to determine / derive heart sounds at predefined time intervals 1200. For example, heart sounds mapped by differential piezoelectric signals are shown in Figure 5. By deriving the interval between the first heart sound (S1) 501 and the second heart sound (S2) 502, the pulse transit time (PTT) 503 can be determined. Step 12b: For example, estimating central pressure based on the PTT 503 from the apex 601 to the suprasternal notch 602 when the sensor assembly is positioned over two locations, the apex 601 and the suprasternal notch 602 (as shown in FIG. 16 ). Step 12c: The waveforms for the predefined time intervals may be used to plot a central hemodynamic profile. As shown in FIG. 7, time interval C 1300 corresponds to vasodilation receiving a high pressure pulse, and time interval D 1302 corresponds to vasoconstriction widening the pulse. The waveforms generated from the signals received from the FSR apex and FSR suprasternal notch may have peak amplitudes for the calibrated predefined time intervals. The program 1102 displays a demo signal with vertical lines at predefined positions that may be determined in the area above the actual signal and display the measurement location of each parameter. This may be an image from the patient's last recording. As shown in FIG. 7, "A" 1304 corresponds to the calibrated peak amplitude of dilation, and "B" 1306 corresponds to the peak amplitude of contraction. The program 1102 may determine or derive the elasticity of the blood vessel by taking the ratio of A:B, and the program may determine or derive an index of blood vessel compliance by taking the ratio of (A / C):(B / D). When comparing the ratios (A / C):(B / D) of different locations, a hemodynamic diagram of the patient or the clinician treating the patient can be plotted. The processor 1100 or program 1102 may be configured to derive 1200 the amplitude ratio of the subtracted calibrated piezoelectric waveform (SN-Apex) at the suprasternal notch and the apex, which can be correlated with the ejection fraction 1308. The program 1102 can also have a means 1700 of notifying the patient or user. Upon an alert from the system or notifying the patient to see the clinician for a regular check-up, the clinician can measure the calibrated ejection time / refill time ratio with other verification methods, such as ultrasound or other imaging devices, to verify the results determined by the use of the device with the sensor assembly. Step 12d: Calculating the ejection fraction 1308. The program 1102 can move a pair of vertical measurement lines to each location on the demo street and take measurements. Using the derived / calibrated SN-apex piezoelectric waveform as shown in Figure 8, the ejection fraction 1308 can be determined. Once the program 1102 derives 1200 data values ​​for A and B, the ejection fraction 1308 can be calculated using the formula: ejection fraction = B as % of A. Step 12e: Calibrating the waveforms for blood pressure estimation. As shown in FIG. 17, examples showing predefined peaks of pressure waveforms 4707 from the piezoelectric sensor 1006 and 4708 from the FSR 1004 obtained in single point calibration 1210 can be compared to beat-by-beat non-invasive blood pressure measurements 4709. Step 12f: Determining tissue compliance 4809. As shown in Figure 18, an example of different tissue compliance such as chest (top waveform) versus wrist (bottom waveform) is shown. Compliance can be measured by the mean Fb minus the mean Fc 4807, 4808. Another step that may be utilized by the program may be to determine cardiac function or portions such as: By selecting a predetermined placement of the sensor assembly on an identified area of ​​interest, as shown in FIG. 6, the sensor is able to obtain information about the following valves: Aortic Valve 1900: S2 component of the heart sound; determines the time interval during which the semilunar valves (aortic and pulmonary) are closed. Pulmonary valve 1902: S1 component of heart sound Tricuspid valve 1904: the time interval during which the tricuspid valve is closed; an S1 heart sound signal can be received. Mitral valve 1906: time interval when the mitral valve (M1) closes; S1 heart sound is louder than S2; lub and dab sounds are received - if a sensor is placed over the mitral valve in a child instead of an adult, an S3 or third heart sound may be generated, which may normally be heard in a child. For example, instead of lub-dab in an adult, it may be heard as lub-dab-dab; S4 may be heard simply as S1 (late diastole); S4 may be low-pitched or galloping.

[0091] Another step that may be utilized by the program 1102 may be to determine when the waveform may be derived based on a reference signal that the program allows for comparison and matching so that associated predetermined intervals such as amplitude or time intervals may be calibrated or calculated or derived 1200. The program 1102 may freeze the lines and values ​​when matched with a reference position.

[0092] Another step that may be utilized by the program 1102 may be that once all values ​​have been measured with the data entered, the data may be loaded onto a cloud or server or storage medium 1170. These data may be categorized as historical data values ​​1108 and may be retrieved by the program 1102 for comparison to current data 1106 or measurements to determine if a particular parameter or factor is deteriorating over time.

[0093] Another step that may be utilized by the program 1102 may be to be able to map or display a graph for each parameter that includes historical values ​​over time.

[0094] Another step that may be utilized by the program 1102 may be that actions from the care plan 1600 may be loaded into the same graphs, e.g., medications, exercises similar to Table 1. The care plan 1600 is dynamically updated in response to the current measurements or data values ​​1106 based on improved measurements or relatively poor measurements compared to previous uses of the sensor assembly 1002. It will be appreciated that the care plan may also be updated manually by a clinician.

[0095] Once the sensor assembly 1002 is coupled to the patient at another predetermined location, the processor may repeat steps 1-12f and other above-described steps, if applicable.

[0096] In another preferred embodiment of the present invention, the processor 1100 may utilize artificial intelligence (AI) software 1102 or computer programs 1102 that may be configured to learn various data patterns and insights.

[0097] FIG. 25A shows a computer screen image of a sensor signal 2500 that is automatically annotated for various morphological features that correspond to important cardiopulmonary events. These features are then used to calculate relative timing, amplitude, slope or area that corresponds to important cardiopulmonary parameters. Examples of these are displayed below and include respiratory rate 2508, aortic volume shift 2510, cardiac conduction time 2512, ejection time 2514, and refill time 2516. Heart rate and heart rate variability are also shown. Other parameters that may be added include changes in tidal volume, respiratory effort, ejection fraction, blood pressure, pulse transit time, pulse wave velocity, contractility and pre-ejection period.

[0098] FIG. 25B shows a screen 2520 with ellipses shown on a graph that highlight rejected artifacts that reduce the measurement of variance of various calculated parameters. These are shown as crosses corresponding to specific features. This process is applicable to any cardiopulmonary feature that can be detected as in FIG. 25A. This process can be performed using manual processing or automatic processing using software from a processor.

[0099] FIG. 25C shows a screen 2540 and instructions for recalculating various parameters after artifact rejection as in FIG. 25B. Once an artifact is rejected, a method is used to replace the feature with a value that fits the duration of the signal being used in the analysis. This may be halfway between the previous and subsequent features (if only one feature in one row is rejected), or multiple features may be replaced by a number equal to the number being replaced, for example, if a duration calculation is required, 20% of the duration between the previous and subsequent features if five in one row are rejected. Another method may be to replace each with the mean value. Once this artifact rejection replacement is performed, new mean and variance measures can be recalculated, and the resulting variance measure is reduced (this is also displaced in the figures shown in 25A and 25B).

[0100] FIG. 25D shows a screen 2560 of a computer-based analysis software system that automatically removes artifactual portions of the signal before estimating variance measurements. Removal of artifacts reduces the variance measurements of cardiac parametric calculations. The variance measurements of important cardiac parameters, i.e., aortic volume shift, cardiac conduction time, ejection time, etc., are within clinically acceptable ranges, and thus the sensory data and associated analytics are proven to be acceptable by clinicians.

[0101] As shown in FIG. 25E, FIG. 25E shows a screen 2580 of a computer-based analysis software system displaying the effect of artifacts on cardiac parametric calculations in real sensory signals after automatic removal of significant artifacts. Rapid template matching using machine learning is advantageous to automatically eliminate artifacts. This exemplary diagram shows a selection template 2598 as a gold standard respiratory cycle based on a piezoelectric sensor. This template 2598 is used to score each recorded respiratory cycle based on similarity. A higher similarity score represents a better match and thus indicates a good cycle, when the data can be clinically accepted. On the other hand, a lower similarity score indicates potential artifacts or other types of noisy signals and cycles.

[0102] As shown in FIG. 25F, FIG. 25F shows a screen 2600 of a computer-based analysis software system, which displays an example of cardiac parametric calculations in real sensory signals, illustrating a methodology for automatically rejecting artifactual periods in recorded time series signals. Matching analysis based on machine learning or machine learning engines includes advanced motif template matching, using the angular positions of multiple feature points within a time period defined by a respiratory cycle. This automatic visual approach mimics a cognitive interactive method to match and eliminate artifacts. The similarity score is the predicted result of a trained learning algorithm that automatically identifies artifactual periods that should be removed to organize the time series data. This can be seen around the boxes indicated as 2610, 2620, 2630, 2640, 2650 during a given period of the screen 2600 where the piezoelectric sensor 2602, ECG lead 2604 are at least used.

[0103] 25G, the automated machine learning engine can use a template 2700 matching algorithm, which can be optimized, to find all valid respiratory cycles 2701 present in the recorded time series data 2702 related to respiratory cycles via the acquired signals from the piezoelectric sensor. In a preferred embodiment, the automated machine learning engine can have the following list of predefined feature thresholds, such as, but not limited to: Maximum time width (S~E) approximately 3.9 seconds; (2704) Maximum amplitude / height approx. 0.07 volts; (2706) The time distance between individual peaks P1 and P2 is approximately 0.6 seconds; (2708) The time distance between individual peaks P2 and P3 is approximately 0.6 seconds; (2710) Standard deviation (σ) of cardiac conduction time is about ±1.66; (2712) Standard deviation (σ) of recharge time is about ±28.3; (2714) Standard deviation (σ) of ejection time is about ±7.68; (2716) Standard deviation (σ) of aortic volume shift is approximately ±47.35; (2718) Heart rate variability (HRV) in segments S to E was approximately 684 ms (2720) Root mean square of successive differences (RMSSD) within segments S to E is approximately 40 ms; (2722), where

number

number

[0104] As shown in FIG. 25H, a decision flow diagram 2800 of an artifact rejection methodology is shown. Machine learning based matching analysis includes advanced motif template matching using angular positions of multiple feature points within a time period defined by a respiratory cycle. This automatic visual matching approach mimics a cognitive interactive method to match and reject artifacts. The similarity score is a predicted result of a trained machine learning algorithm that automatically identifies artifact periods that should be removed to organize the time series data. The training input data 2802 is communicated to a machine learning engine 2804, which is a supervised machine learning (SML) methodology as an artifact prediction model. The training input data 2802 can have a series of peaks 2808 where the processor can detect significant associated peaks, and is processed to identify 2810 peaks and time-based respiratory cycles (RCs). The processor then normalizes 2814 the feature space representing the RCs using any of a described list of predefined feature thresholds associated with a plurality of feature calculations 2812 for each of the RCs, and the processor then performs a motif sequence search 2816 using the RC template 2818 to identify 2820 RCs with high similarity scores, with the similarity threshold 2822 set to 75% or higher. If the similarity is 75% or higher, the system classifies the RC as a valid respiratory cycle 2824, and if the similarity is less than 75%, the system classifies the RC as an invalid respiratory cycle or artifact 2826. The system then annotates 2830 and can be part of the training targets 2828 communicated to the machine learning engine 2804, and the prediction-based artifact detection and rejection 2806 optimizes and refines the time series data.

[0105] As shown in FIG. 19 and FIG. 20, which show the overall system of interest for early heart failure detection, physiological parameters related to heart failure collected from the sensor assembly 1002 can be used to calculate an Early Warning Score for patients at risk for heart failure. The goal and benefit of early detection (before the heart failure system) is to calculate the likelihood that the patient has heart failure at a particular time leading to early intervention. The Early Warning Score based on Artificial Intelligence (EWS AI) also includes phenotypic data from the individual including family history, medical history and other symptoms. The goal of EWS AI is earlier detection for earlier intervention and better patient outcomes resulting from the use of this system. Patients who may be identified as having heart failure can be monitored 2200. The patient can place one or more sensor assemblies 1002 at any of the predetermined locations to obtain the patient's hemodynamic profile. Physiological signals sensed by the sensor assembly 1002 can be transmitted to the processor 1100 where the digital processing unit can display the data on a smartphone or notification system 1700. The unreadable text and graphs are merely representative of the type of information that may be displayed on a smartphone. The analyzed data may then be sent to and from the cloud 2004 or server, or to an artificial intelligence program or application 2000, which may be stored and analyzed. The analyzed data may then be analyzed by the program 2000 or displayed on the clinician portal 1800. For example, the analyzed data may be presented in the form of a graph 2300 as shown in FIG. 20 for the clinician to read the analyzed likelihood of heart failure percentage 2302 over a pre-symptomatic time frame or time interval 2304. The graph 2302 may have at least one parameter of the following groups: physiological 2306, physiological and phenotype 2308, physiological and phenotype and AI 2310. The diagnostic region or interval 2312 may be highlighted for easy viewing by the patient and / or clinician, and the resulting analysis of the percentage likelihood of heart failure 2302 may be communicated to the patient.It will be appreciated that this is not the only graph displayed, but other graphs may be displayed that provide meaningful information for the clinician to proactively assess and manage the patient's cardiac health. In view of the analyzed data, the processor 1100 may create a care plan, which may be reviewed by the clinician who may update or optimize the care plan 1600 based on the analyzed data 1850 or results and / or consultation, and other parameters related to the patient may be recorded or entered into the program 1002 or artificial intelligence application 2000 for further processing. The consultation may be in person or remotely, such as a telemedicine consultation 1852 / 1584. Once the care plan 1600 is created and relayed to the patient, the patient may follow the care plan 1600 and the effect of the care plan may be monitored over a predetermined time period 1860. Here, past analysis data on cardiac function and condition may be compared to current measurements.

[0106] Although the present invention has been described with reference to particular examples, it will be understood by those skilled in the art that the invention can be embodied in many other forms consistent with the broad principles and spirit of the invention as described herein.

[0107] The present invention and the preferred embodiments described specifically include at least one feature that has industrial applicability.

Claims

1. 1. A device for hemodynamic monitoring, comprising: the device comprises a processor having a digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having two or more sensors; the processor is configured to trigger a synchronization signal to the two or more sensors of the first sensor assembly to perform measurements simultaneously, the two or more sensors including a piezoelectric sensor and a piezoresistive sensor; The processor includes a program having executable instructions, the program, when executed on the processor, synchronizing the processed signals of each sensor assembly by calculating a phase shift from the piezoelectric sensor relative to the piezoresistive sensor; mapping the synchronized signals as waveforms for each sensor; performing cardiac cycle template matching to identify signal portions as waveforms associated with the identified cardiac cycle; annotating morphological features; calculating at least one cardiac function parameter as a data value over predetermined time intervals of the waveform; normalizing the calculated time intervals to a constant cardiac cycle length; calculating an average for each of said predetermined normalized time intervals; and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine the cardiac condition.

2. 1. A device for hemodynamic monitoring, comprising: the device comprises a processor having a digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having two or more sensors; the processor is configured to trigger a synchronization signal to the two or more sensors of the first sensor assembly to perform measurements simultaneously, the two or more sensors including a piezoelectric sensor and a piezoresistive sensor; The processor includes a program having executable instructions, the program, when executed on the processor, synchronizing the processed signals of each sensor assembly by calculating a phase shift from the piezoelectric sensor relative to the piezoresistive sensor; mapping the synchronized signals as waveforms for each sensor; performing cardiac cycle template matching to identify signal portions as waveforms associated with the identified cardiac cycle; annotating morphological features; determining a waveform amplitude and calculating at least one cardiac function parameter as a data value; calculating an average for each of the predetermined waveform amplitudes; and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine the cardiac condition.

3. 10. The device of claim 1, wherein the sensor assembly comprises a force sensing resistor, a displacement sensor, and electrocardiogram electrodes, the electrocardiogram electrodes being embedded in the sensor assembly.

4. 4. The device of claim 3, further comprising a non-transitory memory configured to enable the processor to store a status data value corresponding to a first use of the at least one sensor assembly in a predetermined location.

5. 5. The device of claim 4, wherein the differential analyzing step includes comparing second status data values ​​corresponding to a second use of the at least one sensor assembly at the predetermined location and determining a progression of a cardiac condition based on a difference in status data values ​​from the first use and the second use.

6. The device of claim 5 , wherein the processor is further configured to provide a warning to the subject if the determined progress is worse compared to previous use.

7. the differential analyzing step includes determining heart rate variability, pre-ejection period, isovolumic contraction time, ejection time, and cardiac conduction time based on physiological signals received from a first sensor assembly when the sensor assembly is positioned on or near the subject's heart; 2. The device of claim 1, wherein the differential analysis step includes determining events from the cardiac cycle based on the received physiological signals, and the determined events correspond to at least one cardiac function selected from the group consisting of semilunar valve closure, ventricular blood refill period, cardiac conduction time, isovolumic contraction time, cardiac output valve opening, cardiac output valve closure, and ejection period from cardiac output.

8. The device of claim 7 , further comprising a second sensor assembly comprising a second group of sensors, the second sensor assembly being located at a different predetermined location than the first sensor assembly.

9. the first sensor assembly is positioned on the heart and the second sensor assembly is positioned on an arm, and the processor is configured to derive at least one selected from the group consisting of a duration of a first and / or second heart sound and a duration of the cardiac phase; the differential analysis step includes deriving a pulse transit time; 9. The device of claim 8, wherein the differential analyzing step further comprises deriving central blood pressure and vascular stiffness based on the relative timing between opening and closing of the aortic valve and the waveform shape of the received physiological signal.

10. the calculating step includes calculating an average for each predetermined waveform amplitude; the differential analysis step further includes deriving a difference in the amplitude of the force sensor over a predetermined time interval in the force signal to determine data values ​​for A) a calibrated peak amplitude of expansion, and B) a calibrated peak amplitude of contraction; the different analyzing step includes deriving the elasticity of the blood vessel based on the ratio of A:B; the differential analyzing step further includes determining a timing difference of the received signals from the displacement sensor relative to the electrocardiogram electrodes to determine data values ​​for C) a time when the blood vessel dilates, and D) a time when the blood vessel contracts; 10. The device of claim 9, wherein the differential analysis step further comprises generating an index of central hemodynamic function of the subject from the ratio of (A / C):(B / D) obtained from different predetermined locations of the sensor assembly.

11. the processor is configured to trigger a synchronization signal to the first sensor assembly and the second sensor assembly at two different locations to perform measurements simultaneously; 3. The device of claim 2, wherein the differential analyzing step further comprises determining an ejection fraction of the heart based on subtracting physiological signals from the displacement sensors in the first sensor assembly and the second sensor assembly.

12. 9. The device of claim 8, wherein the first sensor assembly is positioned above the apex of the heart and the second sensor assembly is positioned above the suprasternal notch or above the aortic arch, such that the processor is adapted to determine at least one cardiac function by the differential analysis step.

13. 9. The device of claim 8, wherein the first sensor assembly is positioned over the apex of the subject's heart and the second sensor assembly is positioned at an aortic auscultation position of the subject, such that the processor is adapted to determine the at least one cardiac function associated with the left chamber of the heart by the differential analysis step.

14. 9. The device of claim 8, wherein the first sensor assembly is positioned over the apex of the subject's heart and the second sensor assembly is positioned at a pulmonary valve auscultation position of the subject, such that the processor is configured to determine the at least one cardiac function associated with the right chamber of the heart by the differential analysis step.

15. 9. The device of claim 8, wherein the first sensor assembly and the second sensor assembly are positioned at adjacent locations on the top of the heart such that the processor is adapted to derive pulse elasticity and pulse timing, and the processor then determines arterial stiffness and blood pressure by the differential analysis step.

16. 10. The device of claim 1, wherein the differential analyzing step includes determining the state of the jugular venous pulse based on physiological signals received from a first sensor assembly that correlates to various pressure changes in a right atrium when the sensor assembly is positioned on or near a jugular vein in the subject's neck.

17. The device of claim 1 , wherein the processor performs the step of calculating a measure of variance after the step of calculating an average for each of the predetermined normalized time intervals.

18. The device of claim 2 , wherein the processor performs the step of calculating a measure of variance after the step of calculating an average for each of the predetermined waveform amplitudes.

19. The device of claim 17 , wherein the particular morphological features include potential artifact signals.

20. The processor may be configured to normalize the synchronized processed signals between the step of calculating the variance and the step of analyzing the difference, and for normalization the processor may removing the identified potential artifact signal from the synchronized waveform; combining the identified signal portions with the removed artifact signal into the synchronized waveform to form a waveform-organized signal; 20. The device of claim 19, configured to perform the steps of: recalculating from the reduced waveform new mean and variance measurements for each waveform amplitude and time interval, wherein the recalculated time intervals for the at least one cardiac function parameter are normalized to cardiac cycle length.

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