Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6399
Title: Combining recurrent neural networks with eigenvector methods for classification of ECG beats
Authors: Übeyli, Elif Derya
Keywords: Recurrent neural networks
Levenberg-Marquardt algorithm
Eigenvector methods
Electrocardiogram (ECG) beats
Issue Date: 2009
Publisher: Academic Press Inc Elsevier Science
Abstract: The purpose of this study is to evaluate the accuracy of the recurrent neural networks (RNNs) trained with Levenberg-Marquardt algorithm on the electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the RNN trained on the extracted features. The RNNs were implemented for classification of the ECG beats using the statistical features as inputs. The ability of designed and trained Elman RNNs, combined with eigenvector methods, were explored to classify the ECG beats. The classification results demonstrated that the combined eigenvector methods/RNN approach can be useful in analyzing the ECG beats. (C) 2008 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2008.09.002
https://hdl.handle.net/20.500.11851/6399
ISSN: 1051-2004
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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