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Title: Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Authors: Übeyli, Elif Derya
Keywords: Support vector machine (SVM)
Wavelet coefficients
Electrocardiogram (ECG) signals
Post-ictal heart rate oscillations
Partial epilepsy
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: The aim of this study is to evaluate the diagnostic accuracy of the support vector machines (SVMs) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. Post-ictal heart rate oscillations were studied in a heterogeneous group of patients with partial epilepsy. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem, and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features, which well represent the ECG signals, and the SVMs trained on these features achieved high classification accuracies (total classification accuracy was 99.44%). (C) 2008 Elsevier Ltd. All rights reserved.
ISSN: 0952-1976
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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