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Title: Expert systems for time-varying biomedical signals using eigenvector methods
Authors: Güler, İnan
Übeyli, Elif Derya
Keywords: eigenvector methods
combined neural network (CNN)
mixture of experts (ME)
modified mixture of experts (MME)
time-varying biomedical signals
Issue Date: 2007
Publisher: Pergamon-Elsevier Science Ltd
Abstract: In this paper, we present the expert systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN), mixture of experts (ME), and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electroencephalogram signals). Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (CNN, ME) improve the capability of classification of the time-varying biomedical signals. The purpose was to determine an optimum classification scheme for the problem and also to infer clues about the extracted features. Our research demonstrated that the power levels of power spectral density (PSD) estimations obtained by the eigenvector methods are the valuable features which are representing the time-varying biomedical signals and the CNN, ME, and MME trained on these features achieved high classification accuracies. (C) 2006 Elsevier Ltd. All rights reserved.
ISSN: 0957-4174
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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