Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms

Abstract

A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.

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“…Skin temperature, heart rate or respiratory rate are also well-known indicators for the study of stress, emotions or fatigue [55], [56]. Heart rate variability, defined as the variation of the time between heartbeats, has been increasingly used to study stress [57], showing that both are closely connected [58].…”

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