Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7372
Title: Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals
Authors: Übeyli, Elif Derya
Keywords: Recurrent neural networks (RNNs)
Lyapunov exponents
Electrocardiogram (ECG) signals
Chaotic signal
Issue Date: 2010
Publisher: Pergamon-Elsevier Science Ltd
Abstract: An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Recurrent neural network (RNN) was implemented and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat. ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the PhysioBank database were classified. Decision making was performed in two stages: computing features which were then input into the RNN and classification using the RNN trained with the Levenberg-Marquardt algorithm. The research demonstrated that the Lyapunov exponents are the features which are well representing the ECG signals and the RNN trained on these features achieved high classification accuracies (C) 2009 Elsevier Ltd All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2009.06.022
https://hdl.handle.net/20.500.11851/7372
ISSN: 0957-4174
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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