Key result
Calculated HRV indices derived from SCG and ECG demonstrated strong linear correlation, with an R2 of 1.000 for mean interbeat interval using the reference algorithm, though accuracy was reduced by signal noise.
Why the study?
Recent interest has grown in electrode-free heart rate monitoring using seismocardiography, prompting the comparison of heart rate variability indices derived from SCG and ECG and evaluation of heartbeat detectors on SCG signals.
Does seismocardiography (SCG) provide comparable heart rate variability (HRV) indices to standard electrocardiography (ECG) in healthy volunteers?
Observational (n=20)
Does seismocardiography (SCG) provide comparable heart rate variability (HRV) indices to standard electrocardiography (ECG) in healthy volunteers?
Effect estimate: R2 = 1.000
SCG-derived HRV indices show strong linear correlation with ECG-derived indices for mean interbeat interval and frequency-domain parameters, suggesting SCG is a feasible non-invasive alternative for basic HRV monitoring despite susceptibility to noise.
SCG may yield comparable HRV indices to ECG for select parameters; leaves open prospective validation before clinical or research adoption.
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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Sieciński et al. (2019) conducted an observational in Healthy (n=20). Seismocardiography (SCG) beat detection algorithms vs. Electrocardiogram (ECG) was evaluated on Correlation (R2) between HRV indices derived from SCG and ECG (R2 = 1.000). Calculated HRV indices derived from SCG and ECG demonstrated strong linear correlation, with an R2 of 1.000 for mean interbeat interval using the reference algorithm, though accuracy was reduced by signal noise.
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