Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm

According to World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) in 2019, representing 32 % of all global deaths. Of these deaths, 85 % were due to heart attack and stroke. The occurrence and prevalence of atrial fibrillation (AF) is growing worl...

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Main Author:	Lee, Wei Qi
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99515/1/LeeWeiQiMSKE2022.pdf
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spelling	my-utm-ep.995152023-02-27T08:12:17Z Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm 2022 Lee, Wei Qi TK Electrical engineering. Electronics Nuclear engineering According to World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) in 2019, representing 32 % of all global deaths. Of these deaths, 85 % were due to heart attack and stroke. The occurrence and prevalence of atrial fibrillation (AF) is growing worldwide. Limited tools are available to evaluate clinical outcomes and response to thrombolysis in stroke patients with AF. Therefore, this study analysed the ECG features of AF and the normal sinus rhythm signals for AF recognition. The first objective is to extract AF features using second-order dynamic system (SODS) algorithm. The following objective is to investigate the effect of windowing length towards AF classification. Next, to compare the two-pattern recognition machine learning support vector machine (SVM) and artificial neural network (ANN) on the accuracy, specificity, and sensitivity of AF classification. In this study, the ECG signals database from Physiobank included MITBIH Atrial Fibrillation Dataset and MITBIH Normal Sinus Rhythm Dataset are used. For signal pre-processing, butterworth filter are used to diminish the muscle noise and the features signals are extracted by using second order dynamic system. Multiple episodes of the windowing size 2s, 4s, 6s, 8s and 10s included in this design to evaluate the appropriate windowing size for AF signal processing. The pattern recognition machine learning SVM algorithm has higher accuracy compared to ANN accuracy of AF classification, which are having 100 % with 4s windowing size. In conclusion, the 4s windowing size having the highest detection rate in AF classification system. 2022 Thesis http://eprints.utm.my/id/eprint/99515/ http://eprints.utm.my/id/eprint/99515/1/LeeWeiQiMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150032 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Lee, Wei Qi Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
description	According to World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) in 2019, representing 32 % of all global deaths. Of these deaths, 85 % were due to heart attack and stroke. The occurrence and prevalence of atrial fibrillation (AF) is growing worldwide. Limited tools are available to evaluate clinical outcomes and response to thrombolysis in stroke patients with AF. Therefore, this study analysed the ECG features of AF and the normal sinus rhythm signals for AF recognition. The first objective is to extract AF features using second-order dynamic system (SODS) algorithm. The following objective is to investigate the effect of windowing length towards AF classification. Next, to compare the two-pattern recognition machine learning support vector machine (SVM) and artificial neural network (ANN) on the accuracy, specificity, and sensitivity of AF classification. In this study, the ECG signals database from Physiobank included MITBIH Atrial Fibrillation Dataset and MITBIH Normal Sinus Rhythm Dataset are used. For signal pre-processing, butterworth filter are used to diminish the muscle noise and the features signals are extracted by using second order dynamic system. Multiple episodes of the windowing size 2s, 4s, 6s, 8s and 10s included in this design to evaluate the appropriate windowing size for AF signal processing. The pattern recognition machine learning SVM algorithm has higher accuracy compared to ANN accuracy of AF classification, which are having 100 % with 4s windowing size. In conclusion, the 4s windowing size having the highest detection rate in AF classification system.
format	Thesis
qualification_level	Master's degree
author	Lee, Wei Qi
author_facet	Lee, Wei Qi
author_sort	Lee, Wei Qi
title	Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
title_short	Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
title_full	Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
title_fullStr	Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
title_full_unstemmed	Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
title_sort	classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99515/1/LeeWeiQiMSKE2022.pdf
_version_	1776100609385037824

Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm

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