PHONOCARDIOGRAM (PCG) SIGNAL PROCESSING SYSTEMS AND METHODS FOR DETERMINING CARDIAC TISSUE AND VALVULAR BLOOD FLOW PARAMETERS

A non-invasive, passive, and fully automated heart-sound-based system and method that provides estimates for blood velocity, tissue motion, and cardiac chamber size parameters for cardiac assessment is provided. The system uses a computer processor and software to receive PCG acoustic signals from one or more sensors and simultaneously receive electrocardiogram (ECG) signals from one or more sensors that are attached to a patient. The phonocardiogram (PCG) processing system and methods compute proxy metrics for echocardiographic parameters of cardiac tissue motion and valvular blood flow for evaluation.

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BACKGROUND

1. Technical Field

This technology pertains generally to systems and methods for providing cardiac assessments and more particularly to non-invasive devices and methods that produce cardiac assessments by computing values of blood flow velocity and tissue motion from heart sounds.

The heart is a muscle that pumps blood through the body and is divided into four chambers and has four valves as shown schematically inFIG.1AandFIG.1B, respectively. During one cardiac cycle, deoxygenated blood flows from the body into the right side of the heart, routed through the lungs into the left side of the heart and then pumped back out of the heart to the rest of the body as illustrated inFIG.2.

The pumping efficiency of the heart is conventionally measured by calculating the left ventricular (LV) ejection fraction, which is defined as the fraction of blood in the left ventricle that is pumped out from the heart to the body during each cardiac cycle. This measurement can be represented by the equation:

In some cases, the pumping ability of the heart is compromised because the muscles of the left ventricle are unable to relax completely or are too stiff to allow for normal blood filling before pumping, which increases LV filling pressures. This condition is called LV diastolic dysfunction and is one of the biggest reasons for the development of heart failure in humans.

Patients presenting with symptoms of heart failure, such as shortness of breath, fatigue or decreased tolerance to exercise are recommended to undergo an ultrasonic interrogation of the heart, also known as echocardiography. During this imaging technique, a technician visualizes cardiac blood flow and muscle motion and then computes a few parameter values to assess LV dimensions, wall motion, and valvular blood flow patterns. Such echocardiography-based parameters include peak E velocity (the peak velocity of blood flow through the mitral valve during early diastole), E/A ratio (the ratio of early-to-late peak flow velocities through the mitral valve during diastole), e′ velocity (the average flow velocity through the mitral valve during early diastole), peak TR velocity (the peak velocity of blood backflow through the tricuspid valve during systole), and LAVi (the maximum volume of left atrium indexed to the body surface area). Cutoff values for each of these parameters are then analyzed to grade the degree of diastolic dysfunction and to estimate LV filling pressures. While a physician can independently analyze these echocardiographic parameters, the accuracy of calculating these parameters depends on the quality of the echocardiographic images and the level of training and experience of the technician. Echocardiography is therefore a resource-intensive tool.

Although non-invasive systems exist that are capable of cardiac assessment, existing systems like ultrasound are not passive or use modalities other than heart sounds such as, for example, ballistocardiography, pressocardiography, or bio-impedance. Existing heart-sound based non-invasive and passive systems claim capabilities for cardiac assessment and abnormal sound detection in general, and may also identify heart afflictions by name, but they do not explicitly claim the capability of assessing cardiac tissue and blood flow parameter computation or provide any evidence for it. In other words, there exist heart sound-based systems that compute blood volume (such as stroke volume or ejection fraction) and blood pressure (such as systolic or diastolic blood pressure, or pulmonary pressures), but none that compute blood flow velocity (such as velocity of blood flow through a heart valve), cardiac tissue motion (such as deflection of valve or heart tissue), or heart chamber size (such as atrial volume).

Accordingly, there is a need for new systems, devices and schemes to allow reliable and accurate measurements of blood flow velocity, cardiac tissue motion and heart chamber dimensions that are non-invasive, low cost and easy to operate.

BRIEF SUMMARY

A non-invasive and automated phonocardiogram (PCG) processing system and method that computes proxy metrics for echocardiographic parameters of cardiac tissue motion and valvular blood flow is provided. The system uses a computer processor and software to receive PCG acoustic signals from one or more sensors and simultaneously receive electrocardiogram (ECG) signals from one or more sensors that are attached to a patient.

The acquired PCG signals are preferably denoised and processed into one or more of temporal features, amplitude features, frequency features, or spectral entropy features for each heartbeat of the same patient.

Then, the processed features are converted into one of several cardiac tissue and valvular blood flow parameter analogues (proxy metrics) based on a set of predetermined conversion equations. In one embodiment, the amplitude features for all heartbeats of the same patient are processed into a proxy metric for the peak velocity of blood flow through the mitral valve of the patient during early diastole (peak E velocity).

In another embodiment, the extracted frequency features for all heartbeats of the same patient are processed into proxy metrics for the average flow velocity through the mitral valve of the patient during early diastole (e′ velocity) and the maximum volume of the patient’s left atrium indexed to the body surface area (LAVi) of the patient.

In another embodiment the spectral entropy features for all heartbeats of the same subject are processed into a proxy metric for the ratio of early-to-late peak flow velocities through the mitral valve of the patient during diastole (E/A ratio) and the peak velocity of blood backflow through the patient’s tricuspid valve during systole (peak TR velocity).

In contrast to existing methods and systems, this technology provides automated methods and systems for cardiac assessment by computing values of blood flow velocity and tissue motion from acquired heart sound (phonocardiogram, PCG) data. The PCG signal acquisition is passive, i.e., it does not involve any application of energy to the body (unlike echocardiography) and is non-invasive. Instead, the methods simply involve recording sounds generated by the heart over a period of time. The methods and systems presented here require signal processing that is beyond a simple mental process and cannot be done using simple computations or observations. These methods and systems are automated and do not require expert supervision. These signal processing methods and systems have been developed using insights from real-world clinical PCG data. The technology rests on the concept of computing these blood velocity and tissue motion parameters from heart sounds and in their utility in assisting the diagnosis and evaluation of heart disease by health care practitioners.

DETAILED DESCRIPTION

Referring more specifically to the drawings, for illustrative purposes, systems and methods for phonocardiogram (PCG) signal processing that compute proxy metrics for echocardiographic parameters are generally shown. Several embodiments of the technology are described generally inFIG.1AtoFIG.12Bto illustrate the characteristics and functionality of the devices, systems, and methods. It will be appreciated that the methods may vary as to the specific steps and sequence and the systems and apparatus may vary as to structural details without departing from the basic concepts as disclosed herein. The method steps are merely exemplary of the order that these steps may occur. The steps may occur in any order that is desired, such that it still performs the goals of the claimed technology.

Turning now toFIG.4, the general structure of one apparatus and system10for computing proxy metrics for echocardiographic parameters from phonocardiogram (PCG) signals is shown schematically. The apparatus of system10has a control and computing apparatus12with one or more electrocardiogram (ECG) sensors14producing ECG signals as well as one or more phonocardiogram (PCG) sensors16producing PCG signals that are inputs18into the apparatus12. The control/processing apparatus12has at least one processor20, non-transitory memory22and application software24with programming that processes the ECG and PCG signal inputs18as well as extract features, create or apply models and compute proxy metrics as described herein. The apparatus may also have a display26. The display26may be provided for outputting computed analysis or diagnostic results to a health care provider.

In another embodiment, the diagnostic processor may be a separate apparatus or a portion of a the PCG signal processor in which case the application software would also contain instructions for performing the diagnostic processing functions described herein.

In one embodiment, a wearable system10that uses heart sound (phonocardiogram, PCG) signals to compute PCG-based proxies as equivalents for echocardiographic parameters is provided as illustrated generally inFIG.3. This computation leverages PCG-signal analysis techniques that determine physiological characteristics of blood flow and muscle motion, and this fully automated system operates without any expert supervision. The physiological characteristics that are used to compute the PCG-based proxy metrics are essentially the same ones that may be otherwise measured by the echocardiographic parameters. This computation involves sophisticated signal processing that cannot be done simply as a mental process, mere observation or with pen and paper. The heart failure screening accuracy of the resulting proxy metric values are comparable to that of echocardiographic parameters, and this system can be used in a clinical care setting for evaluating heart failure patients.

In the embodiment shown inFIG.5, three ECG sensors14are placed at specific locations30and four PCG acoustic sensors16are placed at other locations32on the left and right sides of the body28of the patient for signal acquisition by the control/processing apparatus12. Although the number of sensors and locations depicted generally inFIG.5are preferred, the number of each sensor type and locations can be varied.

An overview of the sensor signal acquisition and processing steps are set forth generally inFIG.6in the context of use. The processing modules and associated module functions of a PCG signal processor34from patient acquisition to evaluation of the results by the health practitioner42are shown. In this embodiment, the sensor signals from patient28are received by the PCG signal processor34for processing. At the first module36, the signals are optionally improved with filtering and noise subtraction. In one embodiment, noise artifacts from speech, motion and other disturbances are removed from the raw PCG signal using band pass filtering and noise subtraction with a spectral noise subtraction algorithm commonly used in speech processing to obtain a PCG signal of qualitatively higher audio fidelity than raw signals.

The denoised signals are then processed with the heartbeat segmentation and quality assurance module38in the embodiment shown inFIG.6. In this module, the start and end times of individual heartbeats in the PCG signal are identified using the ECG signal as reference. The onset of the R wave in each cardiac cycle of the ECG signal can be regarded as the transition point between the end of one heartbeat and start of the next one.

The quality of individual heart beats may also be considered for identification for further processing. A heartbeat may be considered to be a “quality” heartbeat if its signal: (1) has both S1and S2successfully identified, (2) has systolic and diastolic intervals free of signal excursions, and (3) has a heartbeat duration within ± 20% of median duration for that subject.

The third processing module40is for feature extraction and proxy metric computation. In one embodiment, the PCG signal processor processes the PCG signals to transform the signals into three types of features. It is important to note that features are not segments of the PCG signal, but instead represent a distinguishing property obtained from a segment of the PCG signal. As such, feature computation is not a mental process and cannot be done using simple observation or computation, but instead is done using complex signal processing techniques described here. Once the features are extracted, the PCG signal processor then processes those features to transform the features into PCG-based proxy metrics for each of the echocardiographic parameters. These PCG-based proxy metrics are cardiac tissue and valvular blood flow parameter analogues. Proxy metric computation involves extraction of features that characterize physiological phenomena such as cardiac pressure gradients, tissue motion, and blood flow that are otherwise measured by echocardiographic parameters. In one embodiment, the features include an amplitude feature, a frequency feature, and a spectral entropy feature. In one preferred embodiment, the proxy metrics include peak E velocity, e′ velocity, LAVi, E/A ratio, and peak TR velocity.

An alternative method44for the computation of proxy metrics for echocardiographic parameters from phonocardiogram (PCG) signals is shown inFIG.7. At block46, the PCG acoustic signals from one or more acoustic sensors are received or acquired simultaneously with the electrocardiogram (ECG) signals from one or more sensors attached to the subject.

Optionally, the simultaneously received signals can be denoised at block48with filters or other signal quality improving approaches. In one embodiment, the PCG signals are denoised by applying a band-pass filter with cutoff frequencies of 25 Hz and 140 Hz, and then applying a spectral noise subtraction algorithm which involves estimating the noise spectrum during brief pauses in heart sound activity. Thereafter, subtracting this estimate from the entire signal’s spectrum to obtain a clean heart sound signal at block48.

In another embodiment, a fourth-order Butterworth low-pass filter is applied to the phonocardiogram signal and the resulting signal envelope is divided into frames. The overall noise spectrum is calculated and subtracted from individual discrete Fourier transforms of signal envelope frames and noise free frames added together to reconstruct the phonocardiogram signal from the entire PCG signal.

Other denoising approaches may be taken at block48to produce a clean heart sound signal. A diagram of a PCG signal for a heartbeat that has been filtered and segmented by the PCG signal processor showing diastolic interval, first heart sound (S1), systolic interval, and second heart sound (S2) is illustrated inFIG.9.

At block50ofFIG.7, raw or denoised PCG signals are processed into one or more of a temporal feature, an amplitude feature, a frequency feature, and/or a spectral entropy feature for each heartbeat of the same subject. The feature extraction process may utilize a case-specific selection of either raw or denoised phonocardiogram signals belonging to either all or exclusively quality heartbeats depending on the underlying physiology being characterized.

In one preferred embodiment, the PCG signals are processed into a temporal feature at block50ofFIG.7following the steps shown inFIG.10A. This process60begins with the acquisition of a PCG signal corresponding to one heartbeat at block62. The acquired signal is processed into an amplitude feature by first applying a Hilbert transform on the PCG signal requiring signal processing in both the time and frequency domains at block64. At block66, a low-pass filter with cutoff frequency of 51 Hz is applied, thereby producing a signal envelope. The 60th percentile value of the resulting signal envelope is then calculated for that heartbeat at block68to produce the amplitude feature70.

Referring also toFIG.10B, one embodiment of a process72for producing a frequency feature at block50ofFIG.7is shown. The frequency feature may be processed from a PCG signal acquired at block74corresponding to one heartbeat by applying a Hamming window to a segment of the PCG signal at block76. A 64-point discrete Fourier transform is applied at block78and the center of mass of the frequency distribution between 16 Hz and 160 Hz for that heartbeat is calculated at block80to produce a frequency feature82.

In one preferred embodiment, a spectral feature may be calculated using the process84shown inFIG.10C. A PCG signal corresponding to one heartbeat is acquired at block86and is processed into a spectral entropy feature by first obtaining a signal distribution probability estimate from the PCG signal at block88and calculating a negative product of the signal probability distribution estimate with its logarithm for that heartbeat at block90to produce the spectral entropy feature92.

One or more of these calculated features may then be used to formulate at least one of several proxy metrics as shown in block52ofFIG.7. Although specific processes for producing these features are illustrated, other feature extraction processes may be used to obtain the features at block50for use in formulating proxy metrics at block52.

The proxy metrics selected and formulated at block52ofFIG.7are a measure of physiological phenomena that may be compared to a standard. One or more proxy metrics can be formulated at block52and the status of different aspects of the heart of the subject patient can be evaluated at block54ofFIG.7.

Several transformative PCG-based proxy metrics using the extracted features ofFIG.10AtoFIG.10Care described inFIG.11AthroughFIG.11Eto illustrate the methods. The formulated PCG-based proxy metrics for peak E velocity, e′ velocity, LAVi, E/A ratio, and peak TR velocity may be output to a diagnostic processor configured for assessing diastolic function and left atrial pressure from the PCG-based proxy metrics at block54ofFIG.7.

In one embodiment, a process100for deriving a PCG-based proxy metric for echocardiogram-based peak E velocity parameter110is determined as shown inFIG.11A. The proxy metric is obtained by first acquiring denoised PCG signals for diastolic intervals of quality heartbeats at block102. Ratios of amplitude feature values for pulmonic and aortic signals of each heartbeat are calculated at block104. Then, the mean of the available ratios across all quality heartbeats for each subject is determined at block106. The mean is then fitted to a pre-defined linear model using a predetermined conversion equation at block108ofFIG.11Ato produce the proxy110for the Peak E velocity.

A PCG-based proxy metric for the echocardiogram-based E/A ratio parameter122can be determined with the process112shown inFIG.11B. In this embodiment, the proxy metric for the E/A ratio parameter is determined by obtaining raw pulmonic PCG signals for quality heartbeats at block114. The ratios of the spectral entropy feature values for early and late diastolic intervals of each heartbeat are determined at block116. The mean of the available ratios across all quality heartbeats for each subject is calculated at block118. Then, the calculated mean is fitted to a pre-defined linear model using a pre-determined conversion equation at block120to produce the E/A ratio proxy metric122.

An embodiment124for processing a proxy metric134for an e′ velocity parameter is shown inFIG.11C. Here, the PCG-based proxy metric for the echocardiogram-based e′ velocity parameter is determined by obtaining denoised aortic PCG signals for all heartbeats at block126, calculating frequency feature values for late systolic intervals of each heartbeat at block128, calculating the mean of available ratios across all heartbeats for each subject at block130, and then fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation at block132to produce the proxy for e′ velocity134.

A PCG-based proxy metric for the echocardiogram-based peak TR velocity parameter146can be determined with the process136shown inFIG.11D. In this embodiment, the PCG-based proxy metric for the echocardiogram-based peak TR velocity parameter is determined by obtaining denoised PCG signals for diastolic intervals of quality heartbeats at block138. The ratios of spectral entropy feature values for pulmonic and aortic signals of each heartbeat are then calculated at block140. The mean of available ratios across all quality heartbeats for each subject is calculated at block142. The calculated mean is then fitted to a pre-defined linear model using a pre-determined conversion equation at block144to produce the proxy for peak TR velocity146.

A PCG-based proxy metric for the echocardiogram-based LAVi parameter158can be determined with the process148shown inFIG.11E. In this embodiment, the LAVi parameter is determined by obtaining raw mitral PCG signals for all heartbeats at block150. The frequency feature values for early diastolic intervals of each heartbeat are then calculated at block152. At block154, the mean of available ratios across all heartbeats for each subject is calculated. The calculated mean is fitted to a pre-defined linear model using a pre-determined conversion equation at block156to produce the proxy for LAVi158.

The pre-defined linear models of the processes shown inFIG.11AtoFIG.11Eare preferably generated using training data to determine conversion equations that can be used to compute a proxy metric for any never-seen-before subject, as is the intention of the invention presented here. In other embodiments, signal processing is accompanied with advanced machine learning methods instead of linear regression to provide accurate computation of proxy metrics in these process steps.

In an implementation of this system where a user may select a set of sensors that differ in characteristics from those applied here, it is anticipated that the user must train the system again. This is because it is known that the characteristics of acoustic sensor signals are determined by the specific characteristics of selected sensors. Thus, the linear models and resulting conversion equations must be trained by linear regression with a training dataset obtained with signal acquisition applying the selected sensors on subjects for which corresponding subject condition ground truth has also been obtained.

Accordingly, these PCG-based systems and methods may be used as a part of routine evaluation of patients presenting with symptoms of dyspnea or heart failure and can help them embark on an accelerated path of care. The apparatus and methods are the first of its kind to make non-invasive and passive computations of cardiac tissue and valvular blood flow parameters using heart sound. The end-to-end PCG signal processing, feature extraction, and proxy metric computation algorithms for LV diastolic function evaluation can be operated in a fully automated manner without expert supervision. While the clinical value of the proxy metrics was determined using the 2016 ASE/EACVI algorithm, the proxy metric computation itself was independent of this algorithm and therefore immune to any guideline modification that might be introduced in the future. This demonstrates the utility and potential of the proposed PCG-based system in providing echocardiography-like parameters to the interpreting physician within minutes of signal acquisition in the real-world environment of a hospital or clinic, thereby allowing individuals with heart failure and other cardiac disease to embark on an accelerated path of care.

The technology described herein may be better understood with reference to the accompanying examples, which are intended for purposes of illustration only and should not be construed as in any sense limiting the scope of the technology described herein as defined in the claims appended hereto.

In order to demonstrate the operational principles of the technology, a system was designed and validated using a study population of34adult inpatients scheduled for right heart catheterization at the Oregon Health and Science University Hospital (Portland, OR). Echocardiographic reports consisting of 2-dimensional and Doppler parameters from a transthoracic examination performed in close proximity to the right heart catheterization were obtained for each subject. Each report included one or more of five parameters based on the quality of the echocardiographic study. See (Table 1). The echocardiographic reports for these se34subjects in the subject population constituted the subject condition ground truth data.

The signal acquisition, filtering, heartbeat segmentation and quality assurance features for individuals of this group were also demonstrated and evaluated. During signal acquisition, subjects were lying supine on the catheterization laboratory patient bed.

Phonocardiogram (PCG) signals were acquired at a sample rate of 512 Hz from each subject using four acoustic sensors that were generally placed as illustrated inFIG.5. Each sensor consisted of an electret microphone housed in an acrylonitrile-butadiene-styrene plastic body with a 0.4 mm-thick black nitrile rubber membrane at one end.

Sensors were placed membrane side down by the care provider at the four traditional auscultation points on the chest wall - aortic (second intercostal space, right sternal border), pulmonic (second intercostal space, left sternal border), tricuspid (fourth intercostal space, left sternal border) and mitral (fifth intercostal space, left mid-clavicular line). Locations for sensor placement were determined relative to the suprasternal notch and did not require provider intervention. ECG signals were acquired simultaneously at a sample rate of 300 Hz using three ECG electrodes illustrated inFIG.5. Electrodes were placed proximally on the two upper limbs and (lower left) abdomen. Depending on the catheterization lab schedule, PCG and ECG signal acquisition lasted between 4 and 80 minutes per subject, and these signals were then stored in Matlab (MathWorks, MA) for offline analysis. Example PCG and ECG signal waveforms from consecutive heartbeats are shown inFIG.8. The PCG and ECG data obtained by this signal acquisition for each of the34subjects in the subject population, as described above, together with the subject condition ground truth data described above constituted the training dataset.

A PCG signal processor was designed to perform general PCG signal processing steps of filtering and noise subtraction, heartbeat segmentation and quality assurance, and feature extraction and proxy metric computation as illustrated inFIG.6andFIG.7.

To demonstrate signal filtering and noise subtraction features, the acquired signals from each subject were denoised. Noise artifacts from speech, motion and other disturbances were removed from the raw PCG signal using filtering and noise subtraction to obtain a PCG signal of qualitatively higher audio fidelity than the raw signals. In this illustration, the first step involved applying a band-pass filter with cutoff frequencies of 25 Hz and 140 Hz to retain the maximum amount of heart sound information while removing most low and high-frequency noise artifacts.

The remaining noise artifacts in the mid-frequency range overlapped with the frequency range of heart sound and were instead removed by applying a spectral noise subtraction algorithm commonly used in speech processing. This technique involved estimating the noise spectrum during brief pauses in heart sound activity and then subtracting this estimate from the entire signal’s spectrum to obtain a clean heart sound signal. Regions of the PCG signal corresponding to pauses in heart sound activity were identified in the amplitude distribution of the signal envelope on a frame-by-frame basis. For this purpose, a fourth-order Butterworth low-pass filter with a cutoff frequency of 38 Hz was applied to the phonocardiogram signal. The signal envelope obtained as a result was then divided into 93 millisecond-long frames with 31 millisecond (33%) overlaps between adjacent frames. The frame and overlap length were empirically determined to provide the best noise subtraction.

Next, the amplitude distribution for the signal envelope was obtained by arranging the root-mean-square amplitude values of individual frames in increasing order. This amplitude distribution had a roughly bimodal shape with one peak for lower amplitude values corresponding to pauses in heart sound activity and another peak for higher amplitude values corresponding to physiological and pathological heart sounds.

The individual frequency spectra for selected frames in the first peak were calculated using a discrete Fourier transform and these spectra were then averaged to approximate one overall noise spectrum for that PCG signal. This average noise spectrum was then subtracted from individual discrete Fourier transforms of all available signal envelope frames, including those belonging to heart sound activity, and its corresponding time-domain signal was recovered by performing an inverse Fourier transform.

Noise-free signals in each frame were then added together while accounting for the original 33% overlap to reconstruct the phonocardiogram signal for the entire PCG signal. Finally, the same band-pass filter with cutoff frequencies of 25 Hz and 140 Hz was then applied again and the resulting signal was now noise-free.

Heartbeat segmentation and quality assurance features as applied to the denoised or raw signals were also demonstrated. For segmentation, the start and end times of individual heartbeats in the processed PCG signals were identified using the ECG signal as a reference. The onset of the R wave in each cardiac cycle of the ECG signal was regarded as the transition point between the end of one heartbeat and start of the next one. The ECG signal between two consecutive onset points was then identified as one cardiac cycle, and the corresponding PCG signal was therefore identified as one heartbeat. The short-time periodicity of the cardiac cycle, which existed even in severely afflicted cases, was then leveraged to identify the first (S1) and second (S2) heart sounds within each heartbeat. For this, the PCG signal was divided into overlapping frames containing two heartbeats each with one beat of overlap in between consecutive frames, and the signal envelope of each frame was calculated using a low-pass filter with a cutoff frequency of 10 Hz.

A cross correlation was then performed for each frame with a comb function of values zero at all points except t=0 and t=T (where T was the time period of the first of the two heartbeats). The location of impulses in this function were expected to be close to the onset of the first S1peak in the PCG signal frame, and the location of the second S2peak was T seconds after the first S1peak. Once both S1peak locations were known, the start and end time for the S1heart sounds were determined by searching backwards and forwards form the peak to the time point corresponding to 60% peak height. A similar method was used to identify the second heart sound, however this time the signal envelope was computed using a cutoff frequency of 15 Hz. Previous signal analysis experiments revealed that S2peaks were found within 0.2 T and 0.55 T seconds of the S1peak and therefore the peak in the cross-correlation time series occurring in this time interval after the first most prominent peak informed the location of the S2peak of the first heartbeat. The start and end times for S2heart sounds were determined in a similar manner as above.

The identification of the endpoints of S1and S2heart sounds allowed for identification of systolic and diastolic intervals in between these heart sounds, as illustrated inFIG.9. The segmented denoised signal was used to mark corresponding endpoint in the raw PCG signal. Not all heartbeats in the resulting dataset were perfectly segmented and a subset of high-quality heartbeats was therefore created for applications where the entire dataset of heartbeats was not necessary. A heartbeat was considered a quality heartbeat if its signal: (1) had both S1and S2successfully identified, (2) has systolic and diastolic intervals free of signal excursions, and (3) had a heartbeat duration within ± 20% of median duration for that subject.

To demonstrate the feature extraction process of the methods, the acquired and processed signals of the set of initial subjects were used as the source. The feature extraction process utilized a case-specific selection of either raw or denoised phonocardiogram signals belonging to either all or exclusively quality heartbeats depending on the underlying physiology being characterized.

In this Example, the PCG signal processor processed the PCG signals to transform the signals into three types of features including an amplitude feature, a frequency feature, and a spectral entropy feature. These features were then available to formulate proxy metrics including peak E velocity, e′ velocity, LAVi, E/A ratio, and peak TR velocity.

The amplitude feature in this Example was generated by applying a Hilbert transform to the selected PCG signal segment and then applying a low-pass filter with cutoff frequency of 51 Hz, thereby producing a signal envelope, as illustrated in process ofFIG.10A. The 60th percentile value of the resulting signal envelope was then calculated and designated as the amplitude feature. This amplitude feature was subsequently used to compute the proxy metric for the peak E velocity parameter. PCG-based proxy metric computation was carried out on a per-heartbeat basis.

The frequency feature was generated by applying a Hamming window to the PCG signal segment and then applying a 64-point discrete Fourier transform following the process ofFIG.10B. The center of mass of the frequency distribution between 16 Hz and 160 Hz was then calculated and designated as the frequency feature. The frequency feature was subsequently used to compute proxy metrics for the e′ velocity and LAVi parameters. PCG-based proxy metric computation was carried out on a per-heartbeat basis.

Finally, the spectral entropy feature was generated by obtaining a signal distribution probability estimate from the PCG signal segment and then calculating the negative product of the signal probability distribution estimate with its logarithm as described in the process illustrated inFIG.10C. The spectral entropy feature was subsequently used to compute proxy metrics for the E/A ratio and peak TR velocity parameters. PCG-based proxy metric computation was carried out on a per-heartbeat basis.

To demonstrate the formulation of proxy metrics, a final feature value for each subject was calculated by taking the mean feature value of select heartbeats for that subject. Noise-subtraction, heartbeat segmentation, feature extraction and proxy metric computation proceeded in a fully automated manner. The extracted features directly characterized physiological phenomena otherwise measured by echocardiographic parameters. A summary of features used for each echocardiographic parameter is shown in Table 2 and the processes used are generally described inFIG.11AthroughFIG.11E.

The per-subject feature values were plotted against their echocardiographic parameters, and the proxy metric was estimated for each subject using a linear fit. The linear fit involved first establishing a model of the relationship between the echocardiographic parameter and the PCG feature by computing the constants (slope and intercept) of the linear regression between the two, and then using these calculated constants to estimate a proxy echocardiographic metric for any given PCG feature value. The proxy metric was adjusted by subtracting the linear model’s intercept and dividing by its slope, and any proxy values outside physiologically-feasible ranges were truncated accordingly. It will be seen that these pre-defined linear models can be used to compute a proxy metric for any never-seen-before subject and new model derivations may not be needed with every analysis.

The proxy for Peak E velocity was determined by obtaining denoised PCG signals for diastolic intervals of quality heartbeats, calculating ratios of amplitude feature values for pulmonic and aortic signals, calculating the mean of the available ratios for each subject, and fitting the calculated mean to a linear model as illustrated inFIG.11A.

The Peak E velocity parameter was a measure of peak early diastolic flow velocity at the mitral valve leaflet tips during passive emptying of the left atrium into the left ventricle. The value of this parameter reflected the pressure gradient between the left atrium and left ventricle and was affected by any alterations in the rate of left ventricular relaxation or left atrial pressure. Subjects in the set with high flow velocities showed corresponding high signal amplitudes. The ratio of pulmonic-to-aortic amplitude-based features calculated for the diastolic denoised phonocardiogram signals in high-quality beats was therefore chosen to characterize this trend.

Since the direction of diastolic blood flow was away from the location of aortic and pulmonic auscultation points, lower amplitude-based feature values were seen for high peak E velocity values. The ratio of pulmonic-to-aortic feature values here allowed for the comparison of this trend on the left and right sides of the heart. As a result, greater diastolic amplitude ratios were seen for subjects with larger peak E velocity values, and this ratio was therefore chosen to compute the proxy metric for the peak E velocity parameter.

The proxy for the E/A ratio was determined by obtaining raw pulmonic PCG signals for quality heartbeats, calculating ratios of spectral entropy feature values for early and late diastolic intervals, calculating the mean of the available ratios for each subject, and fitting the calculated mean to a linear model as illustrated inFIG.11B.

The E/A ratio parameter was a measure of the ratio of early-to-late peak diastolic flow velocities at the mitral valve leaflet tips during the passive and subsequent active emptying of the left atrium into the left ventricle. The value of this parameter was used to identify the state of left ventricular function: normal, impaired relaxation, moderate diastolic dysfunction (pseudoformal filling), or restrictive left ventricular filling (impaired left ventricular compliance).

Diastolic phonocardiogram signal segments associated with left ventricular filling-related muscular contractions were identified using the spectral-entropy based feature. Lower spectral entropy values were seen in late-diastolic signal segments corresponding to active left atrial contractions when compared to early-diastolic signal segments corresponding to passive left atrial emptying. This trend was strongest for raw phonocardiogram signals in high-quality heartbeats acquired at the pulmonic auscultation point. A ratio of early-to-late pulmonic diastolic signal spectral entropy-based features was therefore chosen to compute the proxy metric for the E/A ratio parameter.

The proxy for e′ velocity was determined by obtaining denoised aortic PCG signals for all heartbeats, calculating frequency feature values for late systolic intervals, calculating the mean of available ratios for each subject, and fitting the calculated mean to a linear model as illustrated inFIG.11C.

The e′ velocity parameter was a measure of the average early diastolic flow velocity at the mitral valve annulus during passive emptying of the left atrium into the left ventricle. The value of this parameter was seen to be associated with the time constant of left ventricular relaxation. The left ventricular hemodynamic forces responsible for these early-diastolic mitral annulus deflections were indirectly estimated during systole.

While high-frequency vibrations associated with high-velocity blood flow showed corresponding elevated levels of high-frequency signal content, low-frequency vibrations associated with cardiac muscle motion showed corresponding elevated levels of low-frequency signal content. Subjects with high e′ velocity values due to larger mitral annulus deflections also showed greater muscle motion-related low frequency content during systole. This phenomenon was characterized by calculating the frequency-based feature for the denoised end-systolic phonocardiogram signals in all heartbeats acquired at the aortic auscultation point. This feature was therefore chosen to compute the proxy metric for the e′ velocity parameter.

The proxy for peak TR velocity was determined by obtaining denoised PCG signals for diastolic intervals of quality heartbeats, calculating ratios of spectral entropy feature values for pulmonic and aortic signals, calculating the mean of available ratios for each subject, and fitting the calculated mean to a linear model as shown inFIG.11D.

The Peak TR velocity parameter was a measure of the peak regurgitant systolic jet velocity at the tricuspid valve during right ventricular contraction. The value of this parameter provided an indirect measure of the pulmonary artery systolic pressure which was seen to be directly correlated to left atrial pressure. Subjects with greater peak TR velocity values and therefore higher pulmonary artery pressures have been observed to show organized heart sound patterns in phonocardiogram signals collected at the pulmonic auscultation point. These patterns were characterized by calculating the ratio of the spectral entropy-based feature for the diastolic interval phonocardiogram signal acquired at the pulmonic and aortic auscultation points.

Lower spectral entropy values were seen at the pulmonic auscultation point for subjects with greater peak TR velocity values. This trend was strongest for denoised phonocardiogram signals in high-quality heartbeats, and this ratio was therefore chosen to compute the proxy metric for the peak TR velocity parameter.

The proxy for LAVi was determined by obtaining raw mitral PCG signals for all heartbeats, calculating frequency feature values for early diastolic intervals, calculating the mean of available ratios for each subject, and fitting the calculated mean to a linear model as illustrated inFIG.11E.

The LAVi parameter was a measure of the maximum left atrial volume indexed to body surface area. The value of this parameter reflected the cumulative effects of increased left atrial pressures over time. Subjects with greater LAVi values and therefore larger left atria showed greater muscle-motion related low-frequency signal content during left ventricular filling in early diastole. This trend was characterized by calculating the frequency-based feature for the early diastolic interval phonocardiogram signal acquired at the mitral auscultation point and was strongest for raw phonocardiogram signals in all heartbeats. This feature was therefore chosen to compute the proxy metric for the LAVi parameter.

Proxy metrics could not be calculated for all subjects due to occasional signal quality deficiencies associated with measurement in the noisy catheterization laboratory environment. Peak E velocity and peak TR velocity proxies were unavailable for 6 subjects each, e′ velocity for three subjects, and LAVi for one subject.

A comparison of the proxy metric results with existing echocardiography and standards was conducted to verify the diagnostic accuracy of the apparatus and methods. The clinical value of proxy metrics was evaluated using a diagnostic processor employing a customized diagnostic algorithm based on the algorithm described in the joint recommendations of the American Society of Echocardiography (ASE) and the European Association of Cardiovascular Imaging (EACVI) in 2016.

The diagram shown inFIG.12Arepresents elements of an algorithm for the assessment of left ventricular diastolic dysfunction in patients with normal left ventricular ejection fraction (LVEF). This algorithm was customized to use peak E velocity, e′ velocity, peak TR velocity and LAVi to identify subjects with LV diastolic dysfunction in the presence of normal LV ejection fraction values. The standard parameters that were used are shown inFIG.12A. Referring also toFIG.12B, the second part of the algorithm was customized to use the above 4 parameters along with E/A ratio to estimate the mean left atrial pressure (as an indirect measure of LV filling pressure) for subjects with reduced ejection fraction values or those with normal ejection fraction values in presence of underlying myocardial disease. The parameters used are shown in the diagram ofFIG.12B.

The goal of this Example was to compare PCG-based proxy metrics with echocardiographic parameters for LV diastolic function assessment using this 2016 ASE/EACVI algorithm. Ground truth diastolic dysfunction and left atrial pressure evaluations were obtained for each subject using their echocardiographic parameters irrespective of their ejection fraction value.

Proxy metrics identified LV diastolic dysfunction in29subjects with 87.5% accuracy, and elevated LV filling pressures in17subjects with 75% accuracy. These numbers were closely in line with those reported in reference studies comparing diagnostic accuracy of echocardiographic parameters with gold-standard invasive-catheter pressure measurements. Potential sources of error in proxy metric computation were that PCG signals were not recorded concurrently with echocardiographic parameters or due to occasional signal quality deficiencies during measurement in the noisy catheterization laboratory environment.

A system for computing proxy metrics of echocardiographic parameters, comprising: (a) one or more phonocardiogram (PCG) sensors; (b) one or more electrocardiogram (ECG) sensors; (c) a computer processor; and (d) a non-transitory memory storing instructions executable by the computer processor; (e) wherein the instructions, when executed by the processor, cause the processor to perform steps comprising: (i) receiving PCG acoustic signals from one or more PCG sensors attached to a subject; (ii) receiving simultaneously electrocardiogram (ECG) signals from one or more ECG sensors attached to the subject; (iii) optionally denoising the received PCG acoustic signals; (iv) processing the PCG signals into one or more of temporal features, amplitude features, frequency features, or spectral entropy features for each heartbeat of the subject; and (v) converting the processed features into one or more proxy metrics of cardiac tissue and valvular blood flow parameters with a set of predetermined conversion equations.

The system of any preceding or following implementation, wherein denoising of the received PCG acoustic signals comprises: applying a band-pass filter with cutoff frequencies of 25 Hz and 140 Hz to the PCG acoustic signals; estimating a spectral noise spectrum during brief pauses in heart sound activity; and subtracting the estimate from a spectrum of the whole signal to obtain a clean heart sound signal.

The system of any preceding or following implementation, wherein the instructions when, executed by the processor, further perform steps comprising: identifying start and end times of individual heartbeats in PCG signals using ECG signals as a reference; identifying first (S1) and second (S2) heart sounds, and diastolic and systolic intervals within each identified heartbeat; and assessing whether a heartbeat qualifies as a quality heartbeat by determining whether both S1and S2have been successfully identified, determining whether systolic and diastolic intervals are free of signal excursions, and determining whether the heartbeat duration is within ± 20% of median duration for the subject.

The system of any preceding or following implementation, wherein the amplitude feature processing comprises applying a Hilbert transform on the PCG signal with signal processing in both the time and frequency domains; applying a low-pass filter with cutoff frequency of 51 Hz, thereby producing a signal envelope, and calculating the 60th percentile value of the resulting signal envelope for that heartbeat.

The system of any preceding or following implementation, wherein the frequency feature processing comprises isolating a PCG signal corresponding to one heartbeat; applying a Hamming window to a segment of the PCG signal; applying a 64-point discrete Fourier transform, and calculating a center of mass of a frequency distribution between 16 Hz and 160 Hz for the heartbeat.

The system of any preceding or following implementation, wherein the spectral entropy frequency feature processing comprises isolating a PCG signal corresponding to one heartbeat; obtaining a signal distribution probability estimate from the PCG signal; and calculating a negative product of the signal probability distribution estimate with its logarithm for that heartbeat.

The system of any preceding or following implementation, wherein each conversion equation is generated using linear regression applied to a training dataset of subject condition ground truth data and sensor signal data obtained for a subject population.

The system of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for echocardiogram-based peak E velocity parameter comprises: obtaining denoised PCG signals for diastolic intervals; identifying quality heartbeats; calculating ratios of amplitude feature values for pulmonic and aortic signals of each heartbeat; calculating a mean of available ratios across all quality heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The system of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiogram-based E/A ratio parameter comprises obtaining raw pulmonic PCG signals for quality heartbeats; calculating ratios of spectral entropy feature values for early and late diastolic intervals of each heartbeat; calculating the mean of the available ratios across all quality heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The system of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiogram-based e′ velocity parameter comprises obtaining denoised aortic PCG signals for all heartbeats; calculating frequency feature values for late systolic intervals of each heartbeat; calculating a mean of available ratios across all heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The system of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiogram-based peak TR velocity parameter comprises obtaining denoised PCG signals for diastolic intervals of quality heartbeats; calculating ratios of spectral entropy feature values for pulmonic and aortic signals of each heartbeat; calculating the mean of available ratios across all quality heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The system of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiography-based LAVi parameter comprises obtaining raw mitral PCG signals for all heartbeats; calculating frequency feature values for early diastolic intervals of each heartbeat; calculating the mean of available ratios across all heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The system of any preceding or following implementation, wherein the PCG-based proxy metrics for peak E velocity, e′ velocity, LAVi, E/A ratio, and peak TR velocity are output to a diagnostic processor configured for assessing diastolic function and left atrial pressure from the PCG-based proxy metrics.

A method for computing proxy metrics for echocardiographic parameters, the method comprising: (a) receiving PCG acoustic signals from one or more sensors attached to a subject; (b) receiving simultaneously electrocardiogram (ECG) signals from one or more sensors attached to the subject; (c) processing the denoised PCG signals into one or more of temporal features, amplitude features, frequency features, or spectral entropy features for each heartbeat of the same subject; (d) converting the extracted features into a plurality of cardiac tissue and valvular blood flow parameter analogues (proxy metrics) based on a set of predetermined conversion equations, the conversions comprising; (i) processing the amplitude features for all heartbeats of the same subject into a proxy metric for the peak velocity of blood flow through the subject’s mitral valve during early diastole (peak E velocity); (ii) processing the frequency features for all heartbeats of the same subject into proxy metrics for the average flow velocity through the subject’s mitral valve during early diastole (e′ velocity) and the maximum volume of the subject’s left atrium indexed to the subject’s body surface area (LAVi); and (iii) processing the spectral entropy features for all heartbeats of the same subject into a proxy metric for the ratio of early-to-late peak flow velocities through the subject’s mitral valve during diastole (E/A ratio) and the peak velocity of blood backflow through the subject’s tricuspid valve during systole (peak TR velocity).

The method of any preceding or following implementation, further comprising denoising of the received PCG acoustic signals, the denoising comprising applying a band-pass filter with cutoff frequencies of 25 Hz and 140 Hz to the PCG acoustic signals; estimating a spectral noise spectrum during brief pauses in heart sound activity; and subtracting the estimate from a spectrum of the whole signal to obtain a clean heart sound signal.

The method of any preceding or following implementation, further comprising identifying start and end times of individual heartbeats in PCG signals using ECG signals as a reference; identifying first (S1) and second (S2) heart sounds, and diastolic and systolic intervals within each identified heartbeat; and assessing whether a heartbeat qualifies as a quality heartbeat by determining whether both S1and S2have been successfully identified, determining whether systolic and diastolic intervals are free of signal excursions, and determining whether the heartbeat duration is within ± 20% of median duration for the subject.

The method of any preceding or following implementation, wherein the amplitude feature processing comprises applying a Hilbert transform on the PCG signal with signal processing in both the time and frequency domains; applying a low-pass filter with cutoff frequency of 51 Hz, thereby producing a signal envelope, and calculating the 60th percentile value of the resulting signal envelope for that heartbeat.

The method of any preceding or following implementation, wherein the frequency feature processing comprises isolating a PCG signal corresponding to one heartbeat; applying a Hamming window to a segment of the PCG signal; applying a 64-point discrete Fourier transform, and calculating a center of mass of a frequency distribution between 16 Hz and 160 Hz for the heartbeat.

The method of any preceding or following implementation, wherein the spectral entropy frequency feature processing comprises isolating a PCG signal corresponding to one heartbeat; obtaining a signal distribution probability estimate from the PCG signal; and calculating a negative product of the signal probability distribution estimate with its logarithm for that heartbeat.

The method of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for echocardiogram-based peak E velocity parameter comprises obtaining denoised PCG signals for diastolic intervals; identifying quality heartbeats; calculating ratios of amplitude feature values for pulmonic and aortic signals of each heartbeat; calculating a mean of available ratios across all quality heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The method of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiogram-based E/A ratio parameter comprises obtaining raw pulmonic PCG signals for quality heartbeats; calculating ratios of spectral entropy feature values for early and late diastolic intervals of each heartbeat; calculating the mean of the available ratios across all quality heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The method of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiogram-based e′ velocity parameter comprises obtaining denoised aortic PCG signals for all heartbeats; calculating frequency feature values for late systolic intervals of each heartbeat; calculating a mean of available ratios across all heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The method of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiogram-based peak TR velocity parameter comprises obtaining denoised PCG signals for diastolic intervals of quality heartbeats; calculating ratios of spectral entropy feature values for pulmonic and aortic signals of each heartbeat; calculating the mean of available ratios across all quality heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

The method of any preceding or following implementation, wherein the calculation of a PCG-based proxy metric for the echocardiography-based LAVi parameter comprises obtaining raw mitral PCG signals for all heartbeats; calculating frequency feature values for early diastolic intervals of each heartbeat; calculating the mean of available ratios across all heartbeats for each subject; and fitting the calculated mean to a pre-defined linear model using a pre-determined conversion equation.

As used herein, the term “implementation” is intended to include, without limitation, embodiments, examples, or other forms of practicing the technology described herein.

Phrasing constructs, such as “A, B and/or C”, within the present disclosure describe where either A, B, or C can be present, or any combination of items A, B and C. Phrasing constructs indicating, such as “at least one of” followed by listing a group of elements, indicates that at least one of these group elements is present, which includes any possible combination of the listed elements as applicable.

References in this disclosure referring to “an embodiment”, “at least one embodiment” or similar embodiment wording indicates that a particular feature, structure, or characteristic described in connection with a described embodiment is included in at least one embodiment of the present disclosure. Thus, these various embodiment phrases are not necessarily all referring to the same embodiment, or to a specific embodiment which differs from all the other embodiments being described. The embodiment phrasing should be construed to mean that the particular features, structures, or characteristics of a given embodiment may be combined in any suitable manner in one or more embodiments of the disclosed apparatus, system or method.

The terms “comprises,” “comprising,” “has”, “having,” “includes”, “including,” “contains”, “containing” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises ... a″, “has ... a″, “includes ... a″, “contains ... a″ does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element.

The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed.

Benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential features or elements of the technology describes herein or any or all the claims.

In addition, in the foregoing disclosure various features may grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Inventive subject matter can lie in less than all features of a single disclosed embodiment.

It will be appreciated that the practice of some jurisdictions may require deletion of one or more portions of the disclosure after that application is filed. Accordingly the reader should consult the application as filed for the original content of the disclosure. Any deletion of content of the disclosure should not be construed as a disclaimer, forfeiture or dedication to the public of any subject matter of the application as originally filed.

The following claims are hereby incorporated into the disclosure, with each claim standing on its own as a separately claimed subject matter.

TABLE 1Summary of Parameters available in Echocardiographic ReportsParameterDescriptionNumber of Subjects this Parameter was Available forPeak E velocityThe peak velocity of blood flow through the mitral valve during early diastole26E/A ratioThe ratio of early-to-late peak flow velocities through the mitral valve during diastole18e′ velocityThe average flow velocity through the mitral valve during early diastole23Peak TR velocityThe peak velocity of blood backflow through the tricuspid valve during systole22LAViThe maximum volume of left atrium indexed to the body surface area25

TABLE 2Summary of Features used to Compute Proxies for Echocardiographic ParametersParameterFeature used to Compute Proxy MetricPeak E velocityRatio of pulmonic-to-aortic diastolic amplitude for denoised signal in quality heartbeatsE/A ratioRatio of early-to-late pulmonic diastolic spectral entropy for raw signal in quality heartbeatse′ velocityAortic late-systolic frequency center of mass for denoised signal in all heartbeatsPeak TR velocityRatio of pulmonic-to-aortic diastolic interval spectral entropy for denoised signal in quality heartbeatsLAViMitral early-diastolic frequency center of mass for raw signal in all heartbeats