Heart Arrhythmia Classification based on Nonlinear Analysis and Dynamic Behavior of Heart Rate Variability (HRV)Signal, M.Sc. Thesis Sharif University of Technology ; Bagheri Shouraki, Saeed (Supervisor) ; Ghorshi, Mohammad Ali (Co-Advisor)
Abstract
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...
Cataloging briefHeart Arrhythmia Classification based on Nonlinear Analysis and Dynamic Behavior of Heart Rate Variability (HRV)Signal, M.Sc. Thesis Sharif University of Technology ; Bagheri Shouraki, Saeed (Supervisor) ; Ghorshi, Mohammad Ali (Co-Advisor)
Abstract
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...
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