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Comparison of HRV indices obtained from ECG and SCG signals from CEBS database
Szymon Siecinski1, Ewaryst J Tkacz2,3, Pawel S Kostka1
1Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelt's Street, 41-800, Zabrze, Poland.
Insights
Heart rate variability (HRV) analysis using seismocardiography (SCG) shows similar results to electrocardiography (ECG) heart monitoring. The choice of heart beat detection method influences the correlation of HRV indices derived from SCG signals.
Area of Science:
- Cardiovascular physiology
- Biomedical engineering
- Signal processing
Background:
- Heart rate variability (HRV) assesses autonomic nervous system function.
- Electrode-free heart monitoring is gaining interest.
- Seismocardiography (SCG) non-invasively records heart vibrations using accelerometers.
Purpose of the Study:
- Compare HRV indices from SCG and ECG.
- Evaluate the impact of different heart beat detection algorithms on SCG signals.
- Assess the feasibility of SCG for HRV analysis.
Main Methods:
- Utilized the combined ECG, breathing, and seismocardiogram (CEBS) database.
- Employed two SCG beat detection methods: one referencing ECG R-waves and one using SCG only.
- Performed time and frequency domain HRV analysis.
Main Results:
- SCG beat detection showed good performance (85,954 beats), though noisier signals yielded lower accuracy.
- High correlation ([Formula: see text]) was observed for mean interbeat interval, LF, and HF.
- Robust modeling improved ECG-SCG correlation for most HRV indices, with exceptions for pNN50.
Conclusions:
- HRV indices from SCG closely resemble those from ECG, with minor discrepancies in specific metrics (SDNN, RMSSD, NN50, pNN50, [Formula: see text]).
- The selection of the beat detection algorithm significantly impacts the relationship between ECG- and SCG-derived HRV.
- SCG shows promise as an electrode-free alternative for HRV assessment.
Background:
Heart 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.
Methods:
We 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.
Results:
Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals.
Conclusions:
Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.
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