Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7772
Title: Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Authors: Übeyli, Elif Derya
Keywords: eigenvector methods
electrocardiogram (ECG) beats
multilayer perceptron neural network (MLPNN)
combined neural network (CNN)
mixture of experts (ME)
modified mixture of experts (MME)
probabilistic neural network (PNN)
support vector machine (SVM)
Issue Date: 2008
Publisher: Academic Press Inc Elsevier Science
Abstract: In this paper, implementation of automated diagnostic systems with diverse and composite features for electrocardiogram (ECG) beats was presented and their accuracies were determined. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. 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 were searched for ECG beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features were compared. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems. (C) 2007 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2007.05.005
https://hdl.handle.net/20.500.11851/7772
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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