A model-based Bayesian framework for ECG beat segmentation

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...

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Bibliographic Details
Published inPhysiological measurement Vol. 30; no. 3; pp. 335 - 352
Main Authors Sayadi, O, Shamsollahi, M B
Format Journal Article
LanguageEnglish
Published England IOP Publishing 01.03.2009
Subjects
Algorithms
Bayes Theorem
Computer Simulation
Electrocardiography - methods
Humans
Models, Cardiovascular
Normal Distribution
Signal Processing, Computer-Assisted
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Summary: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.
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ISSN:0967-3334
1361-6579
DOI:10.1088/0967-3334/30/3/008