Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6683
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Güler, İnan | - |
dc.contributor.author | Übeyli, Elif Derya | - |
dc.date.accessioned | 2021-09-11T15:43:10Z | - |
dc.date.available | 2021-09-11T15:43:10Z | - |
dc.date.issued | 2007 | en_US |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.issn | 1873-6793 | - |
dc.identifier.uri | https://doi.org/10.1016/j.eswa.2006.02.002 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6683 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Pergamon-Elsevier Science Ltd | en_US |
dc.relation.ispartof | Expert Systems With Applications | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | eigenvector methods | en_US |
dc.subject | combined neural network (CNN) | en_US |
dc.subject | mixture of experts (ME) | en_US |
dc.subject | modified mixture of experts (MME) | en_US |
dc.subject | time-varying biomedical signals | en_US |
dc.title | Expert Systems for Time-Varying Biomedical Signals Using Eigenvector Methods | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 32 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 1045 | en_US |
dc.identifier.endpage | 1058 | en_US |
dc.identifier.wos | WOS:000243797800009 | en_US |
dc.identifier.scopus | 2-s2.0-33751437697 | en_US |
dc.institutionauthor | Übeyli, Elif Derya | - |
dc.identifier.doi | 10.1016/j.eswa.2006.02.002 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
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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