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A model-based Bayesian framework for ECG beat segmentation

Sayadi, O ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1088/0967-3334/30/3/008
  3. Publisher: 2009
  4. Abstract:
  5. 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. © 2009 Institute of Physics and Engineering in Medicine
  6. Keywords:
  7. Correction algorithm ; ECG dynamical model ; Extended KalmanFilter ; Fiducial points extraction ; Fluctuating estimates ; Segmentation ; algorithm ; article ; Bayes theorem ; electrocardiogram ; mathematical computing ; priority journal ; simulation ; Algorithms ; Bayes Theorem ; Computer Simulation ; Electrocardiography ; Humans ; Models, Cardiovascular ; Normal Distribution ; Signal Processing, Computer-Assisted
  8. Source: Physiological Measurement ; Volume 30, Issue 3 , 2009 , Pages 335-352 ; 09673334 (ISSN)
  9. URL: https://iopscience.iop.org/article/10.1088/0967-3334/30/3/008