title: Applying machine learning to detect individual heart beats in ballistocardiograms.
abstract: Ballistocardiography is a technique in which the mechanical activity of the heart is recorded. We present a novel algorithm for the detection of individual heart beats in ballistocardiograms (BCGs). In a training step, unsupervised learning techniques are used to identify the shape of a single heart beat in the BCG. The learned parameters are combined with so-called "heart valve components" to detect the occurrence of individual heart beats in the signal. A refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to other algorithms this new approach offers heart rate estimates on a beat-to-beat basis and is designed to cope with arrhythmias. The proposed algorithm has been evaluated in laboratory and home settings for its agreement with an ECG reference. A beat-to-beat interval error of 14.16 ms with a coverage of 96.87% was achieved. Averaged over 10 s long epochs, the mean heart rate error was 0.39 bpm.
authors.affiliation: Philips Chair for Medical Information Technology, RWTH Aachen University, Germany. brueser@hia.rwth-aachen.de
authors.forename: Christoph
authors.initials: C
authors.lastname: Bruser
authors.forename: Kurt
authors.initials: K
authors.lastname: Stadlthanner
authors.forename: Andreas
authors.initials: A
authors.lastname: Brauers
authors.forename: Steffen
authors.initials: S
authors.lastname: Leonhardt
mesh_terms: D000328:Adult; D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis / Q000503:physiopathology; D001185:Artificial Intelligence; D001450:Ballistocardiography / Q000379:methods*; D004867:Equipment Design; D005260:Female; D006339:Heart Rate; D006351:Heart Valves / Q000473:pathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors