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dc.contributor.authorGüler, İnan-
dc.contributor.authorÜbeyli, Elif Derya-
dc.identifier.citation2005 ICSC Congress on Computational Intelligence Methods and Applications, 15 December 2005 through 17 December 2005, Istanbul, 69339en_US
dc.identifier.isbn1424400201; 9781424400201-
dc.description.abstractIn this paper, we present the automated diagnostic systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN) and mixture of experts (ME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic 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 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 and ME trained on these features achieved high classification accuracies.en_US
dc.relation.ispartof2005 ICSC Congress on Computational Intelligence Methods and Applicationsen_US
dc.subjectCombined neural network (CNN)en_US
dc.subjectEigenvector methodsen_US
dc.subjectMixture of experts (ME)en_US
dc.subjectTime-varying biomedical signalsen_US
dc.titleEigenvector methods for automated detection of time-varying biomedical signalsen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.institutionauthorÜbeyli, Elif Derya-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference2005 ICSC Congress on Computational Intelligence Methods and Applicationsen_US
item.fulltextNo Fulltext-
item.openairetypeConference Object-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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