Innovative non-contact r-r intervals estimation using viterbi algorithm with Squared Branch Metric (VSBM)
Abstract
Non-contact heartbeat detection by using Doppler sensor is a critical component of remote health monitoring systems, enabling continuous and unobtrusive monitoring of an individual’s cardiovascular health. In this paper, we reported an innovative approach for non-contact heartbeat detection using the Viterbi algorithm, leveraging the distribution of the difference of two adjacent R-R intervals (RRIs). RRIs represent the time between successive peaks in the electrocardiogram (ECG) signal and are fundamental in analyzing heart rate variability, mental stress conditions and heart diseases. Numerous non-contact Doppler sensor-based methods have been proposed for heartbeat detection, leveraging the evaluation of RRIs without physical device attachment. However, challenges arise from unwanted peaks introduced by respiration and slight body movements, even when the subject remains motionless with normal breathing. This study presented an innovative approach for selecting heartbeat peaks utilizing the Viterbi algorithm with the squared difference of two adjacent RRIs as the Branch metric (BM). Our preliminary experiments reveal that the difference between two adjacent RRIs closely follows a Gaussian distribution. Building upon this observation, we considered the Viterbi algorithm with Squared Branch Metric (VSBM) to estimate the heartbeat accurately. To assess the accuracy of our peak selection method, we conducted experiments comparing it with two existing peak detection methods: (i) Doppler output after Low-Pass Filter (LPF)-based method and (ii) Spectrogram-based method. Our results demonstrate that the proposed VSBM method is effective to detect the heartbeat accurately for each peak detection method. Furthermore, we compared the performance of “Spectrogram + VSBM” outperforms the “Doppler output after LPF + VSBM” method by the Root-Mean-Square Error (RMSE) of RRIs.
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