title: 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.
authors.affiliation: Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany. brueser@hia.rwth-aachen.de
authors.forename: Christoph
authors.initials: C
authors.lastname: Brüser
authors.forename: Kurt
authors.initials: K
authors.lastname: Stadlthanner
authors.forename: Stijn
authors.initials: S
authors.lastname: de Waele
authors.forename: Steffen
authors.initials: S
authors.lastname: Leonhardt
mesh_terms: D000222:Adaptation, Physiological; D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans