Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7373
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dc.contributor.authorGüler, N. F.-
dc.contributor.authorÜbeyli, E. D.-
dc.contributor.authorGüler, İnan-
dc.date.accessioned2021-09-11T15:56:40Z-
dc.date.available2021-09-11T15:56:40Z-
dc.date.issued2005en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2005.04.011-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7373-
dc.description.abstractThere are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods, non-parametric methods and several neural network models. Unfortunately, there is no theory available to guide model selection. The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) employing Lyapunov exponents trained with Levenberg-Marquardt algorithm on the electroencephalogram (EEG) signals. An approach based on the consideration that the EEG signals are chaotic signals was used in developing a reliable classification method for electroencephalographic changes. This consideration was tested successfully using the non-linear dynamics tools, like the computation of Lyapunov exponents. We explored the ability of designed and trained Elman RNNs, combined with the Lyapunov exponents, to discriminate the EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures). The RNNs achieved accuracy rates which were higher than that of the feedforward neural network models. The obtained results demonstrated that the proposed RNNs employing the Lyapunov exponents can be useful in analyzing long-term EEG signals for early detection of the electroencephalographic changes. (c) 2005 Elsevier Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectrecurrent neural networksen_US
dc.subjectLevenberg-Marquardt algorithmen_US
dc.subjectelectroencephalogram (EEG) signalsen_US
dc.subjectchaotic signalen_US
dc.subjectLyapunov exponentsen_US
dc.titleRecurrent neural networks employing Lyapunov exponents for EEG signals classificationen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume29en_US
dc.identifier.issue3en_US
dc.identifier.startpage506en_US
dc.identifier.endpage514en_US
dc.identifier.wosWOS:000231659400002en_US
dc.identifier.scopus2-s2.0-24144470790en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1016/j.eswa.2005.04.011-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
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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