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Analyzing Seismocardiographic Approach for Heart Rate Variability Measurement

DOI:10.1016/j.bspc.2021.102793
Authors:
Tilendra Choudhary at Emory University
Tilendra Choudhary
Mousumi Das at Indian Institute of Technology Guwahati
Mousumi Das
L.N. Sharma at Indian Institute of Technology Guwahati
L.N. Sharma
Manas Kamal Bhuyan
Manas Kamal Bhuyan
Manas Kamal Bhuyan
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Abstract and Figures

As a vital risk stratification tool, heart rate variability (HRV) has the ability to provide early warning signs for many life-threatening diseases. This paper presents a study on reliable cardiac cycle extraction and HRV measurement with a seismocardiographic (SCG) method. Like R-peaks in an ECG, the proposed method relies on peaks corresponding to aortic valve opening (AO) instants in an SCG signal. Due to better reliability and accessibility, the SCG signal is selected for the study. Initially, the prominent AO peaks in an SCG signal are estimated using our previously proposed modified variational mode decomposition (MVMD) based approach. In the present method, the detection performance of AO peaks is improved by employing a decision-rule-based post-processing scheme. Subsequently, tachogram of AO–AO intervals is used for the estimation of HRV parameters. A set of real-time signals collected in various physiological conditions and the SCG signals taken from a publicly available standard database are used to test and validate the proposed method. Experimental results clearly tell that the cardiac intervals obtained from the SCG signal using the proposed method can be used for HRV analysis. Also, the resulted parameters of HRV analysis on ECG and SCG exhibit strong correlation and agreement that shows the effectiveness of the proposed method.
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... The addition of a reference instrument (i.e., ECG) leads to an increase in the system overall cost and a decrease in user comfortability. To address these issues, Choudhary et al. [64] proposed an SCG standalone algorithm for HRV analysis which entirely depends on AO peak detection. The algorithm exploits modified variational mode decomposition (MVMD) and a decision-rule-based postprocessing scheme to detect AO peaks and tachogram of AO-AO intervals to estimate HRV indices. ...
... Several studies did not propose an experimental approach but focused on the development of innovative algorithms for data processing and adopted the records contained in publicly available databases to evaluate the performance of the proposed algorithm. For instance, in [46,58,59,64,69], the public CEBS database of PhysioNet containing the combined measurements of ECG, breathing, and SCG was used to test the performance of the proposed algorithm for HR estimation [70]. The details of the main studies that used accelerometers for recording precordial vibrations are schematically reported in Table 2. 14 The private database contains 15 multichannel SCG signals recorded from three healthy male subjects in five different sessions. ...
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... HRV analysis has traditionally been performed on cardiac intervals obtained from electrocardiograms (ECG signals) [13,14,17,18]. The advantage of using seismocardiography is the availability of information on cardiac intervals, contractility, and the state of heart valves [6,13,14,[16][17][18][19]. ...
Comparison of Heart Rate Variability Indices Based on Seismocardiograms from Healthy Volunteers and Patients with Valvular Heart Diseases
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Heart rate variability (HRV) is a physiological variation of intervals between consecutive heart beats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated on the basis of elec-trocardiograms (ECG signals). Because seismocardiog-raphy (SCG) registers cardiac mechanical activity, it may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. In our study, our objective was to compare HRV indices in the time and frequency domain obtained from seismocardiograms (SCG signals) in healthy volunteers and patients with valvular heart diseases. The results of the HRV analysis indicate that there are significant differences between the HRV indices obtained from the seismocardiograms in healthy volunteers and patients with VHD. This shows the feasibility and validity of HRV analysis based on seismocardiograms in healthy volunteers and patients with VHD.
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... According to Tadi et al. [17], Siecinski et al. [22], [24], and Choudhary et al. [16], there is a strong linear correlation between HRV indices obtained from ECG, SCG and GCG signals. Table IV shows Pearson's linear correlation coefficient (ρ) for ECG and SCG signals (ECG-SCG) and ECG and GCG signals (ECG-GCG). ...
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... The proposed denoising framework can be used to recover usable SCG signal from vehicle-corrupted SCG signals. In [72], SCG signals-based method was proposed for the measurement of the heart rate variability (HRV). The AO peak of SCG signal was estimated using a modified variational mode decomposition-based approach combined with a decision-rule-based scheme. ...
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Article
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A Novel Method for Aortic Valve Opening Phase Detection Using SCG Signal
Article
Full-text available
A framework to detect aortic valve opening (AO) phase with the help of seismocardiogram (SCG) signal is proposed. A small electronic circuit board is designed, which consists of 3-D MEMS based accelerometer, pre-amplifier, and filter. It is interfaced with standard data acquisition system to record SCG signals. The signal is decomposed using a proposed modified variational mode decomposition technique. In the first stage of decomposition, baseline drift is suppressed. Whereas, in the second stage, signal information related to AO instants are extracted. Gaussian derivative filtering is performed on each of the decomposed modes to enhance the systolic profiles. These filtered modes are named as Gaussian derivative filtered modes (GDFMs). The GDFMs with probable AO peaks are selected based on proposed relative GDFM energy (RGE). The signal is reconstructed from the selected GDFMs and it is emphasized using the weights derived from squared RGE. The iteratively extracted maximum slope information is incorporated for systole envelope construction. Finally, peaks are detected using Hilbert transform and cardiac cycle envelope. The robustness of the proposed framework is evaluated using clean and noisy SCG signals from two different databases. For publicly available database (CEBS, Physionet), mean detection error rate 5.2%, sensitivity 97.3%, positive predictivity 97.4%, and detection accuracy 95.1% are found. For our real-time SCG database, the values of these metrics are 6.9%, 96.7%, 96.4%, and 93.4%, respectively. The developed system shows good detection rates even on less number of analyzed beats.
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Article
Full-text available
Heart rate variability analysis is a recognized non-invasive tool that is used to assess autonomic nervous system regulation in various clinical settings and medical conditions. A wide variety of HRV analysis methods have been proposed, but they all require a certain number of cardiac beats intervals. There are many ways to record cardiac activity: electrocardiography, phonocardiography, plethysmocardiography, seismocardiography. However, the feasibility of performing HRV analysis with these technologies and particularly their ability to detect autonomic nervous system changes still has to be studied. In this study, we developed a technology allowing the simultaneous monitoring of electrocardiography, phonocardiography, seismocardiography, photoplethysmocardiography and piezoplethysmocardiography and investigated whether these sensors could be used for HRV analysis. We therefore tested the evolution of several HRV parameters computed from several sensors before, during and after a postural change. The main findings of our study is that even if most sensors were suitable for mean HR computation, some of them demonstrated limited agreement for several HRV analyses methods. We also demonstrated that piezoplethysmocardiography showed better agreement with ECG than other sensors for most HRV indexes.
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Heart Rate Variability Analysis on CEBS Database Signals
Presentation
Full-text available
  • Jul 2018
Heart rate variability (HRV) is a valuable non-invasive tool of assessing the state of cardiovascular autonomic function. Over the recent years there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing cardiovascular vibrations. The purpose of this study is to compare HRV indices calculated on SCG and ECG signals from Combined measurement of ECG, breathing and seismocardiogram (CEBS) database. The authors use 20 signals lasting 200 s acquired from patients in supine position and compare heart rate variability parameters from the seismocardiogram and ECG reference signal. They assessed the performance of heart beat detector on SCG channel. The results of modified version of SCG heart beat detection prove its good performance on signals with higher sampling frequency. Strong linear correlation of HRV indices calculated from ECG and SCG prove the reliability of SCG in HRV analysis performed on~signals from CEBS Database.
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Beat-to-beat heart rate detection by smartphone's accelerometers: Validation with ECG
Conference Paper
Full-text available
  • Aug 2016
Methods: 9 healthy male volunteers were studied in supine (SUP) and in standing (ST) posture. A smartphone (iPhone6, Apple) was positioned on the thorax (POS1) to acquire SCG signal. While supine, a second smartphone was positioned on the navel (POS2). The SCG signal was recorded for 3 minutes during spontaneous respiration, synchronous with 3-leads ECG. Using a fully automated algorithm based on amplitude thresholding after rectification, the characteristic peak of the SCG signal (IVC) was detected and used to compute beat-to-beat heart duration, to be compared with the corresponding RR intervals extracted from the ECG. Results: A 100% feasibility of the approach resulted for POS1 in SUP, while 89% in POS2, and 78% for POS1 in ST. In supine, for each smartphones' position, the automated algorithm correctly identified the cardiac beats with >98% accuracy. Linear correlation (r2) with RR was very high (>0.98) in each posture and position, with no bias and narrow limits of agreement. Conclusions: The obtained results proved the feasibility of the proposed approach and the robustness of the applied algorithm in measuring the beat-to-beat heart rate from smartphone-derived SCG, with high accuracy compared to conventional ECG-derived measure.
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Estimation of heart rate and respiratory rate from the seismocardiogram under resting state
Article
This study proposes a dedicated algorithm to estimate heart rate (HR) and respiratory rate (RR) from seismocardiogram (SCG). The proposed algorithm primarily consists of the wavelet decomposition (by db6 wavelet), the Fourier-based envelope detection and the time-averaged power spectral density (PSD) from the scalogram by complex Morlet wavelet. The records in the combined measurement of ECG, breathing and SCG (CEBS) database of PhysioNet are adopted to evaluate the performance of the proposed algorithm for HR and RR estimation. The performance was evaluated by intraclass correlation coefficient (ICC) and Bland-Altman's agreement analysis. The statistical results for HR estimation show good to excellent correlation between the approaches by SCG and ECG and also meet the maximal allowable error for the HR meter. As the quality of some respiratory signals in the CEBS database is not good for RR estimation, additional experiments have been conducted in the university laboratory under well-controlled procedure. The statistical results for the RR estimation in these experiments show good to excellent correlation between the approaches of RR estimation by SCG and respiratory signal. The RR estimation from SCG also meets the specification given in contemporary medical device. The proposed algorithm does not take the removal of motion artifact into account and thus SCG signal must be measured under resting state. From this study, SCG can be a potential tool to estimate HR and RR for monitoring the patient in intensive care unit (ICU) or monitoring the sleep quality in clinical setting or in daily life.
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Orthogonal subspace projection based framework to extract heart cycles from SCG signal
Article
Early diagnosis and prediction of heart diseases are essential to reduce the cardiac risks. Change in heart cycle morphologies is a vital diagnostic feature for cardiac clinical systems. A seismocardiogram (SCG) signal provides more detailed information of different cardiac phases in a heart cycle compared to othercardiac signals. Hence, heart cycle extraction using SCG is very important to examine cardiac activities. In this manuscript, an orthogonal subspace projection based framework is proposed to extract heart cycles from a SCG signal. The heart cycle is estimated by calculating intervals between consecutive aortic valveopening (AO) instants, and post aortic valve closing (postAC) instants. Orthogonal subspace projection is applied to the SCG signal on ECG subspace for AO peak detection. The signal generated from projection gives the locations of AO peaks in the SCG signal. The postAC peaks are determined on intervals between consecutive AO peaks using segmentation, FIR based smoothing, Butterworth high pass filtering, and finding maxima point. The performance of the proposed method is evaluated using SCG signals fromCEBS database, publicly available at Physionet archive. The performance results show that the proposed method produces an acceptable detection rate with a minimal detection error. The evaluation results of the proposed method show its extendibility in heart rate variability analysis and hemodynamic parameter extraction.
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