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Diagnosis of Arrhythmia Using Neural Networks

This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the...

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Main Author: SELEPE, KGAUGELO ZACHARIA
Format: Final Year Project
Language:English
Published: Universiti Teknologi Petronas 2006
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://utpedia.utp.edu.my/6909/1/2006%20-%20Diagnosis%20of%20Arrhythmia%20using%20Neural%20Networks.pdf
http://utpedia.utp.edu.my/6909/
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spelling my-utp-utpedia.69092017-01-25T09:46:17Z http://utpedia.utp.edu.my/6909/ Diagnosis of Arrhythmia Using Neural Networks SELEPE, KGAUGELO ZACHARIA TK Electrical engineering. Electronics Nuclear engineering This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive. Universiti Teknologi Petronas 2006-06 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/6909/1/2006%20-%20Diagnosis%20of%20Arrhythmia%20using%20Neural%20Networks.pdf SELEPE, KGAUGELO ZACHARIA (2006) Diagnosis of Arrhythmia Using Neural Networks. Universiti Teknologi Petronas. (Unpublished)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
SELEPE, KGAUGELO ZACHARIA
Diagnosis of Arrhythmia Using Neural Networks
description This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive.
format Final Year Project
author SELEPE, KGAUGELO ZACHARIA
author_facet SELEPE, KGAUGELO ZACHARIA
author_sort SELEPE, KGAUGELO ZACHARIA
title Diagnosis of Arrhythmia Using Neural Networks
title_short Diagnosis of Arrhythmia Using Neural Networks
title_full Diagnosis of Arrhythmia Using Neural Networks
title_fullStr Diagnosis of Arrhythmia Using Neural Networks
title_full_unstemmed Diagnosis of Arrhythmia Using Neural Networks
title_sort diagnosis of arrhythmia using neural networks
publisher Universiti Teknologi Petronas
publishDate 2006
url http://utpedia.utp.edu.my/6909/1/2006%20-%20Diagnosis%20of%20Arrhythmia%20using%20Neural%20Networks.pdf
http://utpedia.utp.edu.my/6909/
_version_ 1739831400961409024
score 13.211869

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