A novel algorithm for beat-to-beat heart rate estimation in ballistocardiograms achieved a relative beat-to-beat interval error of 1.79% with a coverage of 95.94% compared to an ECG reference.
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.
Brüser et al. (Thu,) conducted a other in Healthy subjects (n=16). Novel algorithm for beat-to-beat heart rate estimation in ballistocardiograms vs. ECG reference was evaluated on Agreement with an ECG reference (relative beat-to-beat interval error and coverage). A novel algorithm for beat-to-beat heart rate estimation in ballistocardiograms achieved a relative beat-to-beat interval error of 1.79% with a coverage of 95.94% compared to an ECG reference.
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