Seismocardiography Signals Research Articles

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology

Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings.

The kinocardiograph for assessment of changes in haemodynamic load in patients with chronic heart failure with reduced ejection fraction.

AimsThe kinocardiograph (KCG) is an unobtrusive device, consisting of a chest sensor, which records local thoracic vibrations produced in result of cardiac contraction and ejection of blood into the great vessels [seismocardiography (SCG)], and a lower back sensor, which records micromovements of the body in reaction to blood flowing through the vasculature [ballistocardiography (BCG)]. SCG and BCG signals are translated to the integral of cardiac kinetic energy (iK) and cardiac maximum power (Pmax), which might be promising metrics for future telemonitoring purposes in heart failure (HF). As a first step of validation, this study aimed to determine whether iK and Pmax are responsive to exercise‐induced changes in the haemodynamic load of the heart in HF patients.Methods and resultsFifteen patients with stable HF with reduced ejection fraction performed a submaximal exercise protocol. KCG and cardiac ultrasound measurements were obtained both at rest and at submaximal exercise. BCG iK over the cardiac cycle (CC) increased significantly (0.0026 ± 0.0017 to 0.0052 ± 0.0061 mJ.s.; P = 0.01) during exercise, in contrast to a non‐significant increase in SCG iK CC. BCG Pmax CC increased significantly (0.92 ± 0.89 to 2.03 ± 1.95 mJ/s; P = 0.02), in contrast to a non‐significant increase in SCG Pmax CC. When analysing the systolic phase of the CC, similar patterns were found. Cardiac output (CO) ratio (i.e. CO exercise/CO rest) showed a moderate, significant correlation with BCG Pmax CC ratio (r = +0.65; P = 0.008) and with SCG Pmax CC ratio (r = +0.54; P = 0.04).Conclusions iK and Pmax measured with the KCG, preferentially using BCG, are responsive to changes in the haemodynamic load of the heart in HF patients. The combination of the BCG and SCG sensor might be of added value to fully understand changes in haemodynamics and to discriminate between an HF patient and a healthy individual.

Advanced Fusion and Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals

Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data.

QUANTIFICATION OF CARDIAC KINETIC ENERGY AND ITS CHANGES DURING TRANSMURAL MYOCARDIAL INFARCTION ASSESSED BY MULTI-DIMENSIONAL SEISMOCARDIOGRAPHY

Objective: Seismocardiography (SCG) records cardiac and blood-induced motion transmitted to the chest surface as vibratory phenomena. Evidences demonstrate that acute myocardial ischemia (AMI) profoundly affects the SCG signals. Multidimensional SCG records cardiac vibrations in linear and rotational dimensions and scalar parameter of kinetic energies can be computed. We speculate that AMI and revascularization profoundly modify cardiac kinetic energy as recorded by SCG. Design and method: Under general anesthesia, 21 swines underwent 90 minutes myocardial ischemia induced by percutaneous subocclusion of proximal left anterior descending (LAD) coronary artery and subsequent revascularization. Invasive hemodynamic parameters were continuously recorded. SCG was recorded during the baseline (BSL); immediately and 80 min after LAD subocclusion (AMI t0; AMIt80, respectively); immediately and 60 min after LAD reperfusion (RE t0, RE t80, respectively). iK was automatically computed for each cardiac cycle (iK_CC) in linear (iK_Lin) and rotational (iK_Rot) dimensions. iK was calculated during systole and diastole (iK_Sys and (iK_Dia, respectively). Results: Compared to baseline, troponins increased from 13.0 [6.5; 21.3] ng/dL to 170.5 [102.5; 475.0] ng/dL and LVEF dropped from 65.0 ± 0.0% to 30.6 ± 5.7% at the end of revascularization (both p < 0.0001). In the linear dimension, iK_Lin-CC, iK_Lin-Sys, iK_Lin-Dia dropped by 43%, 52%, 53% respectively (p < 0.0001, p < 0.0001, p = 0.03, respectively) from baseline to the end of reperfusion. In the rotational dimension, iK_Rot-CC and iK_Rot-Sys dropped by 30% and 36% respectively (p = 0.0006, p < 0.0001), but iK_Rot-Dia did not change (p = 0.41). The figure below represents the evolution of waveforms of iK computed from the SCG signals during the experimental procedure, specifically during BSL, AMIt0, AMIt80, REt0, REt60. Linear (B) and rotational (C) iK drop at AMIt0 compared to BSL, remain below baseline values during the all duration of AMI and returns to normal level during reperfusion. The hemodynamic parameters, except the pulmonary artery pulse pressure, were significantly correlated with parameters of iK. Conclusions: Multidimensional SCG can detect changes in myocardial contractility during experimental AMI, providing information on the cardiac contractile status expressed in term of iK. This automatic system may empower health care providers and patients to remotely monitor cardiovascular status in the near future.

Quantification of Cardiac Kinetic Energy and Its Changes During Transmural Myocardial Infarction Assessed by Multi-Dimensional Seismocardiography.

Introduction: Seismocardiography (SCG) records cardiac and blood-induced motions transmitted to the chest surface as vibratory phenomena. Evidences demonstrate that acute myocardial ischemia (AMI) profoundly affects the SCG signals. Multidimensional SCG records cardiac vibrations in linear and rotational dimensions, and scalar parameters of kinetic energy can be computed. We speculate that AMI and revascularization profoundly modify cardiac kinetic energy as recorded by SCG.Methods: Under general anesthesia, 21 swine underwent 90 min of myocardial ischemia induced by percutaneous sub-occlusion of the proximal left anterior descending (LAD) coronary artery and subsequent revascularization. Invasive hemodynamic parameters were continuously recorded. SCG was recorded during baseline, immediately and 80 min after LAD sub-occlusion, and immediately and 60 min after LAD reperfusion. iK was automatically computed for each cardiac cycle (iKCC) in linear (iKLin) and rotational (iKRot) dimensions. iK was calculated as well during systole and diastole (iKSys and iKDia, respectively). Echocardiography was performed at baseline and after revascularization, and the left ventricle ejection fraction (LVEF) along with regional left ventricle (LV) wall abnormalities were evaluated.Results: Upon LAD sub-occlusion, 77% of STEMI and 24% of NSTEMI were observed. Compared to baseline, troponins increased from 13.0 (6.5; 21.3) ng/dl to 170.5 (102.5; 475.0) ng/dl, and LVEF dropped from 65.0 ± 0.0 to 30.6 ± 5.7% at the end of revascularization (both p < 0.0001). Regional LV wall abnormalities were observed as follows: anterior MI, 17.6% (three out of 17); septal MI, 5.8% (one out of 17); antero-septal MI, 47.1% (eight out of 17); and infero-septal MI, 29.4% (five out of 17). In the linear dimension, , , and dropped by 43, 52, and 53%, respectively (p < 0.0001, p < 0.0001, and p = 0.03, respectively) from baseline to the end of reperfusion. In the rotational dimension, and dropped by 30 and 36%, respectively (p = 0.0006 and p < 0.0001, respectively), but did not change (p = 0.41). All the hemodynamic parameters, except the pulmonary artery pulse pressure, were significantly correlated with the parameters of iK, except for the diastolic component.Conclusions: In this very context of experimental AMI with acute LV regional dysfunction and no concomitant AMI-related heart valve disease, linear and rotational iK parameters, in particular, systolic ones, provide reliable information on LV contractile dysfunction and its effects on the downstream circulation. Multidimensional SCG may provide information on the cardiac contractile status expressed in terms of iK during AMI and reperfusion. This automatic system may empower health care providers and patients to remotely monitor cardiovascular status in the near future.

Assessment of left ventricular twist by 3D ballistocardiography and seismocardiography compared with 2D STI echocardiography in a context of enhanced inotropism in healthy subjects

Ballistocardiography (BCG) and Seismocardiography (SCG) assess the vibrations produced by cardiac contraction and blood flow, respectively, by means of micro-accelerometers and micro-gyroscopes. From the BCG and SCG signals, maximal velocities (VMax), integral of kinetic energy (iK), and maximal power (PMax) can be computed as scalar parameters, both in linear and rotational dimensions. Standard echocardiography and 2-dimensional speckle tracking imaging echocardiography were performed on 34 healthy volunteers who were infused with increasing doses of dobutamine (5–10–20 μg/kg/min). Linear VMax of BCG predicts the rates of left ventricular (LV) twisting and untwisting (both p < 0.0001). The linear PMax of both SCG and BCG and the linear iK of BCG are the best predictors of the LV ejection fraction (LVEF) (p < 0.0001). This result is further confirmed by mathematical models combining the metrics from SCG and BCG signals with heart rate, in which both linear PMax and iK strongly correlate with LVEF (R = 0.7, p < 0.0001). In this setting of enhanced inotropism, the linear VMax of BCG, rather than the VMax of SCG, is the metric which best explains the LV twist mechanics, in particular the rates of twisting and untwisting. PMax and iK metrics are strongly associated with the LVEF and account for 50% of the variance of the LVEF.

Contactless, Battery-free, and Stretchable Wearable for Continuous Recording of Seismocardiograms

Seismocardiography (SCG) is the technique to measure microscale vibrations of the chest, which is closely related to the mechanical activity of the heart. To date, only a few studies have reported technical solutions to remotely measure SCG signals with soft electronics using stretchable, piezoelectric thin films. In this study, however, we demonstrate a contactless wearable patch using a 28-μm-thick foil of the piezoelectric polymer polyvinylidene fluoride. As a technical advancement, we utilize near-field communication (NFC) technology to power the patch and to record the SCG data wirelessly. A separated electrocardiogram (ECG) is used as a reference. Our results show expected waveforms of the recorded SCG signals with good signal-to-noise ratios and a close correlation between SCG and ECG recordings. The SCG technique adds an additional tool for cardiac monitoring, which is tightly associated with an ECG. The SCG method could be combined with other techniques in wearable on-skin electronics with multiple functionality for advanced cardiovascular monitoring.

Quantification of cardiac kinetic energy and its changes during transmural myocardial ischemia assessed by multi-dimensional seismocardiography

Abstract Background The third law of Newton implies that if one body exerts a force on a second body, the latter exerts a force equal in magnitude and opposite in direction on the first body. This reaction to heart and blood motion elicits low frequency vibrations transmitted to the chest surface. Multi-dimensional seismocardiography (SCG) assesses these linear and rotational accelerations by means of micro-accelerometers and micro-gyroscopes. From the linear and rotational acceleration signals, kinetic energy (KE) and its temporal integration (iK) can be computed as scalar parameters, both in a linear (iKLin) and in a rotational (iKRot) dimension. Evidence demonstrate that acute myocardial ischemia (AMI) profoundly affects the SCG waveform amplitude and that unidimensional SCG is accurate for the determination of acute coronary syndrome (ACS). Whether and how AMI affects multidimensional SCG signals is unknwon. Purpose AMI upon left anterior descendent coronary artery (LAD) occlusion markedly depresses iKLin and iKRot. Methods The protocol was in accordance with the principles of laboratory animal care. After 20 minutes of steady-state, anesthetized Landrance pigs underwent a percutaneous mid-LAD occlusion by means of intra-coronary balloon inflation for 90 min., followed by deflation and reperfusion (RE). SCG was recorded at baseline (BSL), upon LAD occlusion (AMIt0), after 60 min of occlusion (AMIt60), at the onset of LAD reperfusion (REt0) and after 60 min of recovery (REt60). iKLin and iKRot were computed from the amplitudes of SCG waveforms. iKLin and iKRot were calculated during the systolic and diastolic phases of the cardiac cycle (iKSys and iKDia, respectively). Firedman's ANOVA was used to test the trend of the above variables throughout the protocol, followed by Wilcoxon paired tests. Data are presented as median [IR]. Results Of the 21 investigated pigs, 3 died before protocol termination as a result of AMI complications and 4 had ruled out during the signal processing because of technical reasons. Considering the rotational variables, when compared to baseline, iKRot_Sys decreased by 17% both at AMIt0 (p=0.05) and AMIt60 (p=0.02); by 38% and 49% at REt0 and t60 (respectively, p&amp;lt;0.001). Similarly, iKRot_Dia decreased by 64% and 59% at AMIt0 and AMIt60 (respectively, p&amp;lt;0.02), and by 74% at REt0 and REt60 (both p=0.002). Considering the linear variables, iKLin_Sys decreased by 19% and 34% at AMIt0 and AMIt60 (respectively, p&amp;lt;0.04), by 39% and 53% at REt0 and REt60 (respectively, p&amp;lt;0.02). iKLin_Dia decreased by 42% at AMIt60 (p=0.03) and by 67% at REt60 (p=0.005) compared to baseline but did not change at the other time points (figure 1). Conclusions AMI depresses profoundly cardiac kinetic energy as recorded by multidimensional SCG. Thus, this nonintrusive and operator independent tool may prove useful in the detection of AMI and provide considerable opportunities for remote non-intrusive monitoring of cardiac functions. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Fonds Erasme, FNRS_Fonds National Recherche Scientifique, Fonds pour la Chirurgie Cardiaque, PRODEXA

Documenting Spatial Variation of SCG Signals for Optimal Sensor Placement

Introduction Seismocardiography (SCG) (low frequency cardiovascular vibrations acquired on the chest wall) has potential for early noninvasive detection of heart failure (HF) deterioration. However, inaccurate sensor placement may significantly limit the method's utility. Objective To document SCG waveform spatial variability to aid in sensor placement and quantification of sensor position shifts on the signal reproducibility. Methods SCG signals were recorded in 15 healthy subjects using 36 accelerometers placed on the anterior chest wall along with simultaneous ECG and respiratory flow measurements (Figure 1). SCG waveforms were segmented using ECG beats. SCG feature points were identified according to previous studies and chest surface acceleration maps created. Results Figure 2 suggests that SCG may be loudest and positive (i.e., anterior or outward) acceleration at the left lower sternal border (LLSB) during the aortic opening and closure periods. A negative (i.e. posterior or inward) acceleration was observed around the LLSB location during the isovolumic contraction time period. The SCG intra-group variability with change in sensor position is shown in Table 1.Table 1: SCG intra-group variability with sensor position change Conclusions Optimal sensor location was found around the LLSB between 3rd to 5th ICS. Use of this location may help increase robustness of SCG signal interpretation and its utility for HF monitoring. Further, change in sensor locations of 3 cm around 4thICS at LLSB (the target sensor location) resulted in only a ∼7 % change in waveform variability and ∼9 % change in PEP, which if true, would better facilitate ambulatory use and sensor placement by patients.

Computational and Mechanical Modeling of SCG Signals

Introduction Seismocardiography (SCG) includes vibration signals propagated to the chest surface from cardiac valve movement, blood flow and myocardial muscle contraction. SCG feature analysis may prove useful in the early non-invasive detection of HF deterioration. In this study, the propagation and coupling of cardiac surface motion to the anterior chest surface was modeled using computer simulations. Objective Develop a computational model of SCG propagation to enhance understanding of SCG genesis, and SCG's spatial amplitude and spectrographic distribution over the anterior chest wall. Methods The computational geometry was extracted from short-axis cardiac cine MRI imaging of a healthy participant. Cardiac wall motion was captured by applying a 3D motion tracking algorithm to the cine MRI images. The propagation of cardiac motion to the chest surface was simulated using Finite Element Method (FEM). A mechanical model was also developed where the displacements at a simulated heart wall were coupled to a mechanical chest surface model using an array of linear actuators. Results Common SCG feature points (e.g., MC-mitral valve closure; IM- Isovolumic contraction; AO- aortic valve opening; AC- aortic valve closure; and MO- mitral valve opening) were predicted by the model. Furthermore, the timing of these feature points was validated against the left ventricle volume variation extracted from short-axis cine MRI images. The maximum SCG amplitude (in the dorso-ventral direction) was observed in the 4th intercoastal space near the left lower sternal border. The pilot results of the mechanical model were in a qualitative agreement. Conclusions The computer model predicted the feature points observed in real SCG measurements. The current model may be utilized in the future to study the distribution of SCG over precordium and to analyze the effect of the properties of cardiac and contiguous chest structures, including great vessels, chest wall ribs, muscles and articulations, lungs, and cutaneous soft tissue. Further model development may deepen understanding of SCG generation and propagation, increasing its potential utility for diagnosis and monitoring of cardiac pathologies.

Harnessing the Manifold Structure of Cardiomechanical Signals for Physiological Monitoring During Hemorrhage.

Local oscillation of the chest wall in response to events during the cardiac cycle may be captured using a sensing modality called seismocardiography (SCG), which is commonly used to infer cardiac time intervals (CTIs) such as the pre-ejection period (PEP). An important factor impeding the ubiquitous application of SCG for cardiac monitoring is that morphological variability of the signals makes consistent inference of CTIs a difficult task in the time-domain. The goal of this work is therefore to enable SCG-based physiological monitoring during trauma-induced hemorrhage using signal dynamics rather than morphological features. We introduce and explore the observation that SCG signals follow a consistent low-dimensional manifold structure during periods of changing PEP induced in a porcine model of trauma injury. Furthermore, we show that the distance traveled along this manifold correlates strongly to changes in PEP ( ∆PEP). ∆PEP estimation during hemorrhage was achieved with a median R2 of 92.5% using a rapid manifold approximation method, comparable to an ISOMAP reference standard, which achieved an R2 of 95.3%. Rapidly approximating the manifold structure of SCG signals allows for physiological inference abstracted from the time-domain, laying the groundwork for robust, morphology-independent processing methods. Ultimately, this work represents an important advancement in SCG processing, enabling future clinical tools for trauma injury management.

Motion artifact cancellation from a single channel SCG using adaptive forgetting factor recursive least square filter

This work proposes a novel filter for motion artifact cancellation from a single channel seismocardiography (SCG) data recorded by a tri-axis accelerometer. A real time adaptive forgetting factor recursive least square filter (AFFRLSF) was developed and embedded in our designed single channel SCG recorder system (SCSRS) for motion artifact cancellation, heartbeat signal extraction, and heart rate calculation. This SCSRS was placed on the chest wall of 24 subjects who were asked to perform standing, walking, jogging, and jumping movements on a treadmill. We recorded the single channel SCG signal and the standard Electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. The heartbeat signals were extracted and heart rates were calculated from the output of AFFRLSF. The graphs of the extracted heartbeat signals were very clear under all the standing, walking, jogging, and jumping motions. The results indicate an average correlation coefficient of up to 0.9878 between heart rates estimated from SCG and ECG of all the 24 subjects. This observation shows that the proposed AFFRLSF could be an effective method for motion artifact cancellation from recorded single channel SCG signals.

Cardiac Function Monitoring for Patients Undergoing Cancer Treatments Using Wearable Seismocardiography: A Proof-of-Concept Study.

Advances in cancer therapeutics have dramatically improved the survival rate and quality of life in patients affected by various cancers, but have been accompanied by treatment-related cardiotoxicity, e.g. left ventricular (LV) dysfunction and/or overt heart failure (HF). Cardiologists thus need to assess cancer treatment-related cardiotoxic risks and have close followups for cancer survivors and patients undergoing cancer treatments using serial echocardiography exams and cardiovascular biomarkers testing. Unfortunately, the cost-prohibitive nature of echocardiography has made these routine follow-ups difficult and not accessible to the growing number of cancer survivors and patients undergoing cancer treatments. There is thus a need to develop a wearable system that can yield similar information at a minimal cost and can be used for remote monitoring of these patients. In this proof-of-concept study, we have investigated the use of wearable seismocardiography (SCG) to monitor LV function non-invasively for patients undergoing cancer treatment. A total of 12 subjects (six with normal LV relaxation, five with impaired relaxation and one with pseudo-normal relaxation) underwent routine echocardiography followed by a standard six-minute walk test. Wearable SCG and electrocardiogram signals were collected during the six-minute walk test and, later, the signal features were compared between subjects with normal and impaired LV relaxation. Pre-ejection period (PEP) from SCG decreased significantly (p < 0.05) during exercise for the subjects with impaired relaxation compared to the subjects with normal relaxation, and changes in PEP/LV ejection time (LVET) were also significantly different between these two groups (p < 0.05). These results suggest that wearable SCG may enable monitoring of patients undergoing cancer treatments by assessing cardiotoxicity.

Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI

Cardiac MRI (CMR) techniques offer non-invasive visualizations of cardiac morphology and function. However, imaging can be time-consuming and complex. Seismocardiography (SCG) measures physical vibrations transmitted through the chest from the beating heart and pulsatile blood flow. SCG signals can be acquired quickly and easily, with inexpensive electronics. This study investigates relationships between CMR metrics of function and SCG signal features. Same-day CMR and SCG data were collected from 28 healthy adults and 6 subjects with aortic valve disease history. Correlation testing and statistical median/decile calculations were performed with data from the healthy cohort. MR-quantified flow and function parameters in the healthy cohort correlated with particular SCG energy levels, such as peak aortic velocity with low-frequency SCG (coefficient 0.43, significance 0.02) and peak flow with high-frequency SCG (coefficient 0.40, significance 0.03). Valve disease-induced flow abnormalities in patients were visualized with MRI, and corresponding abnormalities in SCG signals were identified. This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects. Additionally, through comparison to normative ranges from healthy subjects, it observed correspondences between pathological flow and abnormal SCG. This may support development of an easy clinical test used to identify potential aortic flow abnormalities.

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography.

This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.

Identifying Patients With Coronary Artery Disease Using Rest and Exercise Seismocardiography.

Coronary artery disease (CAD) is the most common cause of death globally. Patients with suspected CAD are usually assessed by exercise electrocardiography (ECG). Subsequent tests, such as coronary angiography and coronary computed tomography angiography (CCTA) are performed to localize the stenosis and to estimate the degree of blockage. The present study describes a non-invasive methodology to identify patients with CAD based on the analysis of both rest and exercise seismocardiography (SCG). SCG is a non-invasive technology for capturing the acceleration of the chest induced by myocardial motion and vibrations. SCG signals were recorded from 185 individuals at rest and immediately after exercise. Two models were developed using the characterization of the rest and exercise SCG signals to identify individuals with CAD. The models were validated against related results from angiography. For the rest model, accuracy was 74%, and sensitivity and specificity were estimated as 75 and 72%, respectively. For the exercise model accuracy, sensitivity, and specificity were 81, 82, and 84%, respectively. The rest and exercise models presented a bootstrap-corrected area under the curve of 0.77 and 0.91, respectively. The discrimination slope was estimated 0.32 for rest model and 0.47 for the exercise model. The difference between the discrimination slopes of these two models was 0.15 (95% CI: 0.10 to 0.23, p < 0.0001). Both rest and exercise models are able to detect CAD with comparable accuracy, sensitivity, and specificity. Performance of SCG is better compared to stress-ECG and it is identical to stress-echocardiography and CCTA. SCG examination is fast, inexpensive, and may even be carried out by laypersons.

Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation.

Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For estimation of the pre-ejection period (PEP), 43% of ICG annotations fell in the corresponding echocardiography ranges while this was 86% for SCG. For estimation of the total systolic time (TST), these numbers were 43, 80, and 90% for ICG, PCG, and SCG, respectively. In summary, SCG and PCG signals provided an acceptable accuracy and precision in estimating cardiac timings, as compared to ICG.

Minimizing Seismocardiography Variability by Accounting for Respiratory Effects

Introduction Seismocardiography (SCG) is a potential method for ambulatory assessment of heart failure (HF) status. New sensor and digital processing capabilities are expanding the possible utility of this approach. The SCG waveform, however, exhibits significant variability to which respiratory effects may be a major contributor. Hence, accounting for respiratory effects may improve SCG waveform interpretation, and support improved utility for HF assessment. Objective The objective of this study was to document SCG waveform respiratory variation and determine optimal criteria for minimizing its impact on signal variability. Methods Air flow and SCG were simultaneously measured in 20 healthy subjects. SCG waveforms were divided into two groups using three different criteria: Inspiration vs. expiration, low vs. high lung volume, and maximum intra-group similarity using Euclidean distance within and between groups. Results The separation between the two groups significantly improved when the maximum intra-group similarity criterion was implemented (p Conclusions Optimal decrease in signal variability was achieved using a maximum intra-group similarity method. Use of this may help increase robustness of SCG signal interpretation and their utility for HF monitoring.

A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning

The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrations of the chest wall due to the cardiac cycle. While BCG is a more well-known modality, it requires the use of a modified bathroom scale or a force plate and cannot be measured in a wearable setting, whereas SCG signals can be measured using wearable accelerometers placed on the sternum. In this paper, we explore the idea of finding a mapping between zero mean and unit l2-norm SCG and BCG signal segments such that, the BCG signal can be acquired using wearable accelerometers (without retaining amplitude information). We use neural networks to find such a mapping and make use of the recently introduced UNet architecture. We trained our models on 26 healthy subjects and tested them on ten subjects. Our results show that we can estimate the aforementioned segments of the BCG signal with a median Pearson correlation coefficient of 0.71 and a median absolute deviation (MAD) of 0.17. Furthermore, our model can estimate the R-I, R-J and R-K timing intervals with median absolute errors (and MAD) of 10.00 (8.90), 6.00 (5.93), and 8.00 (5.93), respectively. We show that using all three axis of the SCG accelerometer produces the best results, whereas the head-to-foot SCG signal produces the best results when a single SCG axis is used.

Influence of Empirical Mode Decomposition on Heart Rate Variability Indices Obtained from Smartphone Seismocardiograms.

Heart rate variability (HRV) is a physiological variation of time interval between consecutive heart beats caused by the activity of autonomic nervous system. Seismocardiography (SCG) is a non-invasive method of analyzing cardiac vibrations and can be used to obtain inter-beat intervals required to perform HRV analysis. Heart beats on SCG signals are detected as the occurrences of aortic valve opening (AO) waves. Morphological variations between subjects complicate developing annotation algorithms. To overcome this obstacle we propose the empirical mode decomposition (EMD) to improve the signal quality. We used two algorithms to determine the influence of EMD on HRV indices: the first algorithm uses a band-pass filter and the second algorithm uses EMD as the first step. Higher beat detection performance was achieved for algorithm with EMD (Se=0.926, PPV=0.926 for all analyzed beats) than the algorithm with a band-pass filter (Se=0.859, PPV=0.855). The influence of analyzed algorithms on HRV indices is low despite the differences of heart beat detection performance between analyzed algorithms.