Comparison of HRV indices obtained from ECG and SCG signals from CEBS database

Szymon Siecinski,Ewaryst J Tkacz,Pawel S Kostka

doi:10.1186/s12938-019-0687-5

Szymon Siecinski, Ewaryst J Tkacz + Show 1 more

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https://doi.org/10.1186/s12938-019-0687-5

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Abstract

BackgroundHeart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. 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 vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals.MethodsWe considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features.ResultsBeat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats (text {Se}=0.930, text {PPV}=0.934) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination (R^2) to determine goodness of fit to linear model. The highest R^2 values were obtained for mean interbeat interval (R^2 = 1.000 for reference algorithm, R^2 = 0.9249 in the worst case), {{text{PSD}}}_{{text{LF}}} and {{text{PSD}}}_{{text{HF}}} (R^2 = 1.000 for the best case, R^2 = 0.9846 for the worst case) and the lowest were obtained for {{text{PSD}}}_{{text{VLF}}} (R^2 = 0.0009 in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the R^2 values of pNN50 values in signals p001–p020 and for all analyzed signals.ConclusionsCalculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and {{text{PSD}}}_{{text{VLF}}}. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.

Highlights

Heart rate variability (HRV) is the physiological phenomenon of variation of time between heartbeats [1], which is caused by the activity of autonomic nervous system [2].HRV has been frequently used in the analysis of physiological signals in different clinical and functional conditions [3, 4]
Due to the lack of annotations of recordings from CEBS database [39], the heart beats in SCG signal were annotated using the algorithm described in “Reference beat detection algorithm”
Tested heart beat detector based on algorithm proposed in paper [24] was evaluated as the number of true positives (TP), false positives (FP), false negatives (FN), the number of beats, sensitivity, and positive predictive value

Summary

Introduction

Heart rate variability (HRV) is the physiological phenomenon of variation of time between heartbeats [1], which is caused by the activity of autonomic nervous system [2].HRV has been frequently used in the analysis of physiological signals in different clinical and functional conditions [3, 4]. Heart rate variability (HRV) is the physiological phenomenon of variation of time between heartbeats [1], which is caused by the activity of autonomic nervous system [2]. There has been interest into non-invasive heart rate monitoring without using electrodes [18]. Seismocardiography (SCG) is a technique of recording and analyzing cardiac activity by measuring precordial acceleration. Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. There has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. We compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals

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