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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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Aortic valve
opening (AO)
instant detection
Proposed Methodology
Display System
DECISION !!!
Signal Acquisition
Mobile/hand-held device
Post-processing
scheme with
decision rules
Time-domain
features
Spectral-domain
features
Non-linear
analysis
HRV Analysis
Creation of
tachogram and
HRV estimation
HRV Visualization
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SCG: AO-AO interval time series
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Mean RR & AOAO (ms)
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100
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AOAO - RR (ms)
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0.02 MD
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(a) Comparison of heartbeats in normal breathing (NB) condition
Bland-Altman plot
Correlation plot
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RR (ms)
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AOAO (ms)
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Mean RR & AOAO (ms)
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0
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200
AOAO - RR (ms)
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0.37 MD
-26 (-1.96SD)
(b) Comparison of heartbeats in stopped breathing (SB) condition
Correlation plot Bland-Altman plot
400 600 800
RR (ms)
400
500
600
700
800
900
AOAO (ms)
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r2=0.97
y=0.98x+10.2
400 600 800
Mean RR & AOAO (ms)
-200
-100
0
100
200
AOAO - RR (ms)
30 (+1.96SD)
-0.57 MD
-31 (-1.96SD)
(c) Comparison of heartbeats in sitting (SI) position
Correlation plot Bland-Altman plot
400 600 800
RR (ms)
400
500
600
700
800
900
AOAO (ms)
RMSE=26 ms
r2=0.94
y=1.00x-1.94
400 600 800
Mean RR & AOAO (ms)
-200
-100
0
100
200
AOAO - RR (ms)
51 (+1.96SD)
-0.47 MD
-52 (-1.96SD)
(d) Comparison of heartbeats in standing (ST) position
Bland-Altman plot
Correlation plot
400 600 800 1000 1200
RR (ms)
400
600
800
1000
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AOAO (ms)
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400 600 800 1000 1200
Mean RR & AOAO (ms)
-200
-100
0
100
200
AOAO - RR (ms)
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0.05 MD
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(e) Comparison of heartbeats in exercise recovery (ER)
Bland-Altman plot
Correlation plot
Hii(i= 1,2,· · · ,8) ith
|ei|=|Hn
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i(SC G)|
5.8×104
4.2×104
meanNN
SDNN
meanHR
SDHR
RMSSD
NN50
HRV TI
LF/HF
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0.2
0.4
0.6
0.8
1
meanNN
SDNN
meanHR
SDHR
RMSSD
NN50
HRV TI
LF/HF
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0.2
0.4
0.6
0.8
Absolute error (n.u.)
(b)
Absolute error in min-max normalized HRV indices derived from AO-AO and R-R interval time series
(a)
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3.5×104
meanNN SDNN meanHR SDHR RMSSD NN50 HRV TI LF/HF
HRV indices
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Normalized cross correlation (NCC)
Correlation in HRV parameters from estimated
AO-AO intervals and RR intervals
0.9461
1
1
0.8342
0.7411
0.6630
0.7225
0.9405
... 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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Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.
... 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]. ...
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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.
... 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). ...
Conference Paper
Heart rate variability (HRV) is a physiological phenomenon of the variation of a cardiac interval (interbeat) over time that reflects the activity of the autonomic nervous system. HRV analysis is usually based on electrocardiograms (ECG signals) and has found many applications in the diagnosis of cardiac diseases, including valvular diseases. This analysis could also be performed on seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) that provide information on cardiac cycles and the state of heart valves. In our study, we sought to evaluate the influence of valvular heart disease on the correlations between HRV indices obtained from electrocardiograms, seismocardiograms, and gyrocardiograms and to compare the HRV indices obtained from the three aforementioned cardiac signals. The results of HRV analysis in the time domain and frequency domain of the ECG, SCG, and GCG signals are within the standard deviation and have a strong linear correlation. This means that despite the influence of VHDs on the SCG and GCG waveforms, the HRV indices are valid. Clinical Relevance—Cardiac mechanical signals (seismocardiograms and gyrocardiograms) can be applied to evaluate heart rate variability despite the influence of valvular diseases on the morphology of cardiac mechanical signals.
... 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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Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan-Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.
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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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Presentation
Full-text available
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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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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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.