Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6550
Title: Diverse and composite features for ECG signals processing
Authors: Übeyli, Elif Derya
Keywords: diverse features
composite features
electrocardiogram (ECG) signals
automated diagnostic systems
wavelet coefficients
eigenvector methods
Issue Date: 2008
Publisher: Ios Press
Abstract: The automated diagnostic systems employing diverse and composite features for electrocardiogram (ECG) signals were analyzed and their accuracies were determined. Because of the importance of making the right decision, classification procedures classifying the ECG signals with high accuracy were investigated. The classification accuracies of multilayer perceptron neural network (MLPNN), recurrent neural network (RNN), and mixture of experts (ME) trained on composite features and modified mixture of experts (MME) trained on diverse features were compared. The inputs of these automated diagnostic systems were composed of diverse or composite features (wavelet coefficients and power levels of the power spectral density estimates obtained by the eigenvector methods) and were chosen according to the network structures. The conclusions of this study demonstrated that the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems trained on composite features.
URI: https://doi.org/10.3233/BME-2008-0509
https://hdl.handle.net/20.500.11851/6550
ISSN: 0959-2989
1878-3619
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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