title: A model-based Bayesian framework for ECG beat segmentation.
abstract: The study of electrocardiogram (ECG) waveform amplitudes, timings and patterns has been the subject of intense research, for it provides a deep insight into the diagnostic features of the heart's functionality. In some recent works, a Bayesian filtering paradigm has been proposed for denoising and compression of ECG signals. In this paper, it is shown that this framework may be effectively used for ECG beat segmentation and extraction of fiducial points. Analytic expressions for the determination of points and intervals are derived and evaluated on various real ECG signals. Simulation results show that the method can contribute to and enhance the clinical ECG beat segmentation performance.
authors.affiliation: Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran. osayadi@ee.sharif.edu
authors.forename: O
authors.initials: O
authors.lastname: Sayadi
authors.forename: M B
authors.initials: MB
authors.lastname: Shamsollahi
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted