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Title: Features for analysis of electrocardiographic changes in partial epileptic patients
Authors: Übeyli, Elif Derya
Keywords: Modified mixture of experts
Eigenvector methods
Wavelet coefficients
Lyapunov exponents
Electrocardiogram (ECG) signals
Post-ictal heart rate oscillations
Partial epilepsy
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
Abstract: In this paper, the usage of features in analysis of electrocardiographic changes in partial epileptic patients was presented. Two types of electrocardiogram (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. The classification accuracies of modified mixture of experts (MME), which were trained on diverse features, were obtained. The eigenvector methods (Pisarenko, multiple signal classification - MUSIC, and Minimum-Norm) were selected to generate the power spectral density (PSD) estimates. The features from the eigenvector PSD estimates, wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 99.44%). (C) 2008 Elsevier Ltd. All rights reserved.
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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