Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6398
Title: Combining eigenvector methods and support vector machines for detecting variability of Doppler ultrasound signals
Authors: Übeyli, Elif Derya
Keywords: Doppler ultrasound signals
eigenvector methods
multiclass support vector machine
(SVM)
Issue Date: 2007
Publisher: Elsevier Ireland Ltd
Abstract: In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for detecting variabilities of the multiclass Doppler ultrasound signals. The ophthalmic arterial (OA) Doppler signals were recorded from healthy subjects, subjects suffering from CA stenosis, subjects suffering from ocular Behcet disease. The internal carotid arterial (ICA) Doppler signals were recorded from healthy subjects, subjects suffering from ICA stenosis, subjects suffering from ICA occlusion. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, better classification procedures for Doppler ultrasound signals are searched. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVMs trained on the extracted features. The research demonstrated that the multiclass SVMs trained on extracted features achieved high accuracy rates. (c) 2007 Elsevier Ireland Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.cmpb.2007.01.006
https://hdl.handle.net/20.500.11851/6398
ISSN: 0169-2607
1872-7565
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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