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Reseach Article

AR Modeling for Cardiac Arrhythmia Classification using MLP Neural Networks

by A.ouelli, B.elhadadi, H.aissaoui, B.bouikhalene
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 24
Year of Publication: 2012
Authors: A.ouelli, B.elhadadi, H.aissaoui, B.bouikhalene
10.5120/7508-0638

A.ouelli, B.elhadadi, H.aissaoui, B.bouikhalene . AR Modeling for Cardiac Arrhythmia Classification using MLP Neural Networks. International Journal of Computer Applications. 47, 24 ( June 2012), 44-51. DOI=10.5120/7508-0638

@article{ 10.5120/7508-0638,
author = { A.ouelli, B.elhadadi, H.aissaoui, B.bouikhalene },
title = { AR Modeling for Cardiac Arrhythmia Classification using MLP Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 24 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 44-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number24/7508-0638/ },
doi = { 10.5120/7508-0638 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:45.281821+05:30
%A A.ouelli
%A B.elhadadi
%A H.aissaoui
%A B.bouikhalene
%T AR Modeling for Cardiac Arrhythmia Classification using MLP Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 24
%P 44-51
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a three stage technique for cardiac arrhythmia classification. This method includes a de-noising module, a feature extraction module and a classification module. In the first module we investigate the application of a FIR least squares filter for noise reduction of the electrocardiogram (ECG) signals. The feature extraction module explores the ability of autoregressive model (AR) to extract relevant features from one-lead electrocardiogram signals in order to classify certain cardiac arrhythmias. Then a number of multilayer perceptron (MLP) neural networks with different number of layers and seven training algorithms are designed. The performances of the networks for speed of convergence and accuracy classifications are evaluated for various ECG data types including normal sinus rhythm, atrial premature contraction, premature ventricular contraction, ventricular tachycardia, ventricular fibrillation and Supraventricular tachycardia obtained from the MIT-BIH database. Among the different training algorithms, the resilient back-propagation (RP) algorithm illustrated the best convergence rate and the Levenberg–Marquardt (LM) algorithm achieved the best overall detection accuracy. The classification accuracies of the six types of arrhythmia were 98. 7% to 100% which is a significant improvement.

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Index Terms

Computer Science
Information Sciences

Keywords

Autoregressive Model Cardiac Arrhythmia Ecg Features Ecg Classification Mlp Rp Algorithm Lm Algorithm Neural Networks Mit-bih Database