Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder

Electrocardiogram (ECG) is an important biomedical tool for the diagnosis of heart disorders. Recent studies have worked a lot on designing automatic diagnosis systems to help physicians. However, automatic study of ECG patterns and heart rate variability is difficult due to the large variation in t...

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Main Author:	Moein, Sara
Format:	Thesis
Published:	2012
Subjects:	QA76.75-76.765 Computer software
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id	my-mmu-ep.5245
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spelling	my-mmu-ep.52452014-02-26T01:58:57Z Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder 2012-09 Moein, Sara QA76.75-76.765 Computer software Electrocardiogram (ECG) is an important biomedical tool for the diagnosis of heart disorders. Recent studies have worked a lot on designing automatic diagnosis systems to help physicians. However, automatic study of ECG patterns and heart rate variability is difficult due to the large variation in the morphologies of heart waveforms, not only of different patients or patient groups but also within the same patient. The main objective of this research work is the automatic normalisation and classification of ECG signals of heart disorders. Two intelligent approaches based on Self Organising Map (SOM) and Particle Swarm Optimisation Neural Network (PSONN) are proposed to find the cutoff frequency for high frequency noise removal in ECG signals. 100 lead II ECG signals were obtained from the Physiobank database, composed of five types of ECG signals, including Normal, Supraventricular tachycardia, Bundle branch block, Anterior myocardial infarction (Anterior MI), and Inferior myocardial infarction (Inferior MI). For each ECG signal, the PSONN and SOM-identified cutoff frequency are applied to a Finite Impulse Response(FIR) filter, and the resulting signal is evaluated against the original clean and conventionally filtered ECG signals. The results show that the proposed intelligent system successfully denoised the ECG signals more effectively than the conventional method. 2012-09 Thesis http://shdl.mmu.edu.my/5245/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php phd doctoral Multimedia University Faculty of Engineering
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Moein, Sara Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
description	Electrocardiogram (ECG) is an important biomedical tool for the diagnosis of heart disorders. Recent studies have worked a lot on designing automatic diagnosis systems to help physicians. However, automatic study of ECG patterns and heart rate variability is difficult due to the large variation in the morphologies of heart waveforms, not only of different patients or patient groups but also within the same patient. The main objective of this research work is the automatic normalisation and classification of ECG signals of heart disorders. Two intelligent approaches based on Self Organising Map (SOM) and Particle Swarm Optimisation Neural Network (PSONN) are proposed to find the cutoff frequency for high frequency noise removal in ECG signals. 100 lead II ECG signals were obtained from the Physiobank database, composed of five types of ECG signals, including Normal, Supraventricular tachycardia, Bundle branch block, Anterior myocardial infarction (Anterior MI), and Inferior myocardial infarction (Inferior MI). For each ECG signal, the PSONN and SOM-identified cutoff frequency are applied to a Finite Impulse Response(FIR) filter, and the resulting signal is evaluated against the original clean and conventionally filtered ECG signals. The results show that the proposed intelligent system successfully denoised the ECG signals more effectively than the conventional method.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Moein, Sara
author_facet	Moein, Sara
author_sort	Moein, Sara
title	Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
title_short	Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
title_full	Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
title_fullStr	Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
title_full_unstemmed	Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
title_sort	kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder
granting_institution	Multimedia University
granting_department	Faculty of Engineering
publishDate	2012
_version_	1747829569686077440

Kinetic gas molecule optimisation neural network for classification of electrocardiogram signals to identify heart disorder

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