Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6263
Title: Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines
Authors: Übeyli, Elif Derya
Keywords: support vector machine (SVM)
eigenvector methods
electrocardiogram (ECG) signals
Issue Date: 2009
Publisher: Wiley
Abstract: In the present study, the diagnostic accuracy of support vector machines (SVMs) on electrocardiogram (ECG) signals is evaluated. Two types of ECG beats (normal and partial epilepsy) were obtained from the Physiobank database. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVM trained on the extracted features. The present research demonstrates that the power levels of the power spectral densities obtained by eigenvector methods are features which represent the ECG signals well and SVMs trained on these features achieve high classification accuracies.
URI: https://doi.org/10.1111/j.1468-0394.2009.00478.x
https://hdl.handle.net/20.500.11851/6263
ISSN: 0266-4720
1468-0394
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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