The week's pick
Reseach Article
AR Modeling for Cardiac Arrhythmia Classification using MLP Neural Networks
| 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
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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