Robust Beat-to-Beat Interval from Wearable PPG using RLS and SSA

Ambulatory Photoplethysmogram (PPG) is a more user-friendly choice for continuous cardiac monitoring as compared to Electrocardiogram (ECG). However, wearable PPG is often prone to motion artefacts. In this paper, we propose a novel pipeline for motion-resistant beat-to-beat interval extraction from noisy PPG. Firstly, the effects of motion artefacts are minimized by using Adaptive Recursive-Least-Square (RLS) Filtering and Singular Spectrum Analysis (SSA). Next, the signal peaks are identified and their locations are corrected by weighted local interpolation. Finally, outlier peak-to-peak intervals are marked as incorrigible. Experimental validation on the training dataset of IEEE Signal Processing Cup 2015 reveals that the proposed method achieves 1.68% mean peak detection error rate and 11.32 milliseconds mean absolute error of detected beat-to-beat intervals. The metric values outperform those obtained by the state-of-the-art techniques by at least 12.58 and 5.74 times respectively.

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