Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin

This thesis presents a research work on a diagnosis system for heart sound based on nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of several heart diseases. Six NARX models which represent Normal and other five categories of heart diseases such as...

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Main Author:	Shamsuddin, Noraishah
Format:	Thesis
Language:	English
Published:	2011
Subjects:	Electric power distribution Electric power transmission
Online Access:	https://ir.uitm.edu.my/id/eprint/6180/2/6180.pdf
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id	my-uitm-ir.6180
record_format	uketd_dc
spelling	my-uitm-ir.61802023-08-22T02:14:24Z Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin 2011 Shamsuddin, Noraishah Electric power distribution. Electric power transmission This thesis presents a research work on a diagnosis system for heart sound based on nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of several heart diseases. Six NARX models which represent Normal and other five categories of heart diseases such as Atrial Septal Defect (ASD), Pulmonary Stenosis (PS), Patent Ductus Arteriosus (PDA), Ventricular Septal Defect (VSD) and Mitral Regurgitation (MR) are estimated. A Lipschitz method and Leven berg Marquardt algorithm is used to determine the model order number and train the network respectively. The R-square value of the OSA prediction of the signal is above 99% for all heart sound signals. The best network architecture for modeling the heart sounds is 2-4-1. As for classification, the features are extracted and selected from the modeled signals and their distinctive patterns are used as inputs to the classifier. To make the system more robust, the background SNR ranging from 3dB to 20dB is injected to the modeled signal. The Resilient Backpropagation (RPROP) algorithm is used to train the network. The optimized learning parameter used is 0.07 and the network has best performance when hidden neurons equal to 220. The architecture of the network is 32-220-6. The accuracy of the network when validated with the diagnostic test is found to be above 97% which suggests that the network performs well and is doing as 'gold standard'. 2011 Thesis https://ir.uitm.edu.my/id/eprint/6180/ https://ir.uitm.edu.my/id/eprint/6180/2/6180.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Taib, Mohd. Nasir
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Taib, Mohd. Nasir
topic	Electric power distribution Electric power transmission
spellingShingle	Electric power distribution Electric power transmission Shamsuddin, Noraishah Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
description	This thesis presents a research work on a diagnosis system for heart sound based on nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of several heart diseases. Six NARX models which represent Normal and other five categories of heart diseases such as Atrial Septal Defect (ASD), Pulmonary Stenosis (PS), Patent Ductus Arteriosus (PDA), Ventricular Septal Defect (VSD) and Mitral Regurgitation (MR) are estimated. A Lipschitz method and Leven berg Marquardt algorithm is used to determine the model order number and train the network respectively. The R-square value of the OSA prediction of the signal is above 99% for all heart sound signals. The best network architecture for modeling the heart sounds is 2-4-1. As for classification, the features are extracted and selected from the modeled signals and their distinctive patterns are used as inputs to the classifier. To make the system more robust, the background SNR ranging from 3dB to 20dB is injected to the modeled signal. The Resilient Backpropagation (RPROP) algorithm is used to train the network. The optimized learning parameter used is 0.07 and the network has best performance when hidden neurons equal to 220. The architecture of the network is 32-220-6. The accuracy of the network when validated with the diagnostic test is found to be above 97% which suggests that the network performs well and is doing as 'gold standard'.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Shamsuddin, Noraishah
author_facet	Shamsuddin, Noraishah
author_sort	Shamsuddin, Noraishah
title	Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
title_short	Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
title_full	Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
title_fullStr	Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
title_full_unstemmed	Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
title_sort	heart sound diagnosis using nonlinear arx model / noraishah shamsuddin
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2011
url	https://ir.uitm.edu.my/id/eprint/6180/2/6180.pdf
_version_	1783733139112198144

Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin

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