Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7375
Title: Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients
Authors: Übeyli, Elif Derya
Keywords: recurrent neural networks (RNNs)
Levenberg-Marquardt algorithm
composite features
wavelet coefficients
Lyapunov exponents
electrocardiogram (ECG) signals
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) with composite features (wavelet coefficients and Lyapunov exponents) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the classification of the ECG signals. Decision making was performed in two stages: computing composite features which were then input into the classifiers and classification using the classifiers trained with the Levenberg-Marquarch algorithm. The research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the ECG signals and the RNN trained on these features achieved high classification accuracies. (C) 2008 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.compbiomed.2008.01.002
https://hdl.handle.net/20.500.11851/7375
ISSN: 0010-4825
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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