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Inter-Beat and Intra-Beat ECG Interval Analysis Based on State Space and Hidden Markov Models

Akhbari, Mahsa | 2016

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  1. Type of Document: Ph.D. Dissertation
  2. Language: English
  3. Document No: 48376 (05)
  4. University: Sharif University of Technology; University of Grenoble Alpes
  5. Department: Electrical Engineering
  6. Advisor(s): Shamsollahi, Mohammad Bagher
  7. Abstract:
  8. Cardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as ECG.In many of these processes, inter-beat and intra-beat features of ECG signal must be extracted. These features include peak, onset and offset of ECG waves,meaningful intervals and segments that can be defined for ECG signal. ECG fiducial point (FP) extraction refers to identifying the location of the peak as well as the onset and offset of the P-wave,QRS complex and T-wave which convey clinically useful information. However, the precise segmentation of each ECG beat is a difficult task, even for experienced cardiologists. In this thesis, we use a Bayesian framework based on the McSharry ECG dynamical model for ECG FP extraction. Since this framework is based on the morphology of ECG waves,it can be useful for ECG segmentation and interval analysis. In order to consider the time sequential property of ECG signal, we also use the Markovian approach and hidden Markov models (HMM). In brief in this thesis, we use dynamic model (extended Kalman filter (EKF)), sequential model (HMM) and their combination (switching Kalman filter (SKF)). We use the proposed methods for ECG FP extraction and ECG interval analysis. Kalmanbased methods are also used for ECG denoising, T-wave alternans (TWA) detection and fetal ECG R-peak detection.To evaluate the performance of proposed methods for ECG FP extraction, we use the “Physionet QT database”, and a “Swine ECG database” that include ECG signal annotations by physicians. For ECG denoising, we use the “MIT-BIH Normal Sinus Rhythm”, “MIT-BIH Arrhythmia” and “MIT-BIH noise stress test” databases. “TWA Challenge 2008 database” is used for TWA detection and finally, “Physionet Computing in Cardiology Challenge 2013 database” is used for R-peak detection of fetal ECG. In ECG FP extraction, the performance of the proposed methods are evaluated in terms of mean, standard deviation and root mean square of error. We also calculate the Sensitivity for FP detection of the different methods. For ECG denoising, we compare the methods in the basis of SNR improvement
  9. Keywords:
  10. Electrocardiogram ; Fiducial Point Extraction ; Extended Kalman Filter ; Hidden Markov Model ; Switching Kalman Filter ; Interval Analysis ; Denoising

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