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Title: Eigenvector Methods for Automated Detection of Electrocardiographic Changes in Partial Epileptic Patients
Authors: Übeyli, Elif Derya
Keywords: Combined neural network (CNN)
electrocardiographic changes
mixture of experts (ME)
modified mixture of experts (MME)
multilayer perceptron neural network (MLPNN)
partial epilepsy
Issue Date: 2009
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Abstract: In this paper, the automated diagnostic systems trained on diverse and composite features were presented for detection of electrocardiographic changes in partial epileptic patients. In practical applications of pattern recognition, there are often diverse features extracted from raw data that require recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Two types (normal and partial epilepsy) of ECG beats (180 records from each class) were obtained from the Physiobank database. The multilayer perceptron neural network (MLPNN), 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 ECG signals, which were trained on diverse or composite features. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The present research demonstrated that the MME trained on the diverse features achieved accuracy rates (total classification accuracy is 99.44%) that were higher than that of the other automated diagnostic systems.,
ISSN: 1089-7771
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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