Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7710
Title: Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents
Authors: Übeyli, Elif Derya
Keywords: multiclass support vector machine (SVM)
Lyapunov exponents
time-varying biomedical signals
Issue Date: 2008
Publisher: Academic Press Inc Elsevier Science
Abstract: In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for the multiclass time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electrocardiogram signals) classification problems. Decision making was performed in two stages: feature extraction by computing the Lyapunov exponents 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 Lyapunov exponents are the features which well represent the studied time-varying biomedical signals and the multiclass SVMs trained on these features achieved high classification accuracies. (C) 2007 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2007.10.001
https://hdl.handle.net/20.500.11851/7710
ISSN: 1051-2004
1095-4333
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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