Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6240
Title: An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet-based neural networks
Authors: Güler, İnan
Übeyli, Derya Elif
Keywords: electrocardiographic changes
multilayer perceptron neural networks
wavelet transform
training algorithms
partial epilepsy
Issue Date: 2005
Publisher: Blackwell Publ Ltd
Abstract: In this study a wavelet-based neural network model, employing the multilayer perceptron, is presented for the detection of electrocardiographic changes in patients with partial epilepsy. Decision making is performed in two stages: feature extraction using the wavelet transform, and multilayer perceptron neural networks (MLPNNs) trained with the backpropagation, delta-bar-delta, extended delta-bar-delta and quick propagation algorithms as classifiers. The classification results, the values of statistical parameters and performance evaluation parameters of the MLPNNs trained with different algorithms are compared. Two types of electrocardiogram beats (normal and partial epilepsy) obtained from the MIT-BIH database were classified with accuracy varying from 90.00% to 97.50% by the MLPNNs trained with different algorithms.
URI: https://doi.org/10.1111/j.1468-0394.2005.00295.x
https://hdl.handle.net/20.500.11851/6240
ISSN: 0266-4720
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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