Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7709
Title: Time-varying biomedical signals analysis with multiclass support vector machines
Authors: Übeyli, Elif Derya
Keywords: multiclass support vector machine (SVM)
wavelet coefficients
Time-varying biomedical signals
Issue Date: 2007
Publisher: Acta Press Anaheim
Source: 5th IASTED International Conference on Biomedical Engineering -- FEB 14-16, 2007 -- Innsbruck, AUSTRIA
Abstract: In this paper, the multiclass support vector machine (SVM) with the error correcting output codes (ECOC) was presented for the multiclass time-varying biomedical signals (electrocardiogram signals) classification problems. 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 research demonstrated that the wavelet coefficients are the features which well represent the studied time-varying biomedical signals and the multiclass SVMs trained on these features achieved high classification accuracies.
URI: https://hdl.handle.net/20.500.11851/7709
ISBN: 978-0-88986-648-5
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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