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Heart Arrhythmia Classification based on Nonlinear Analysis and Dynamic Behavior of Heart Rate Variability (HRV)Signal

Rezaei, Shahab | 2014

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 47024 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Bagheri Shouraki, Saeed; Ghorshi, Mohammad Ali
  7. Abstract:
  8. Detection and classification of arrhythmia is important especially for patients in Emergency care units. Early diagnosis of cardiac arrhythmia makes it possible to choose appropriate anti arrhythmic drugs, and is thus very important for improving arrhythmia therapy. Computer-Assisted Diagnostic (CAD) Systems are used in recent decades in which extracted features and classifiers are the most important factor. In this project, we try to focus on both of these two major factors in heart arrhythmia classification using HRV signal. Therefore, in this project, we try to classify different groups of arrhythmia using HRV signal processing especially the nonlinear processing. Our main aim is to evaluate the dynamic behavior of HRV in normal heart in compare with different arrhythmia and determine the novel features which are able to help the classification of heart arrhythmia in an automatic ways. So the project includes three main stages: 1. Data Gathering, 2. Feature extraction, 3. Fuzzy Classification. In the first stages, the HRV data of different kinds of heart arrhythmia have been gathered. The data for this project are obtained from MIT Physionet Databank. In second stage, different HRV signal processing methods are used on data, such as time domain analysis, nonlinear phase space analysis, and especially some novel methods that have been introduced in recent years for HRV signal processing. In the next stage, these features will be used in fuzzy classifiers for classification of different kinds of arrhythmia. The performance of using classifier is evaluated with parameters such as accuracy, sensitivity and specificity
  9. Keywords:
  10. Dynamic Behavior ; Nonlinear Analysis ; Fuzzy Classifier ; Heart Rate Variability ; Poincare Index

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