Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6334
Full metadata record
DC FieldValueLanguage
dc.contributor.authorÜbeyli, Elif Derya-
dc.date.accessioned2021-09-11T15:35:53Z-
dc.date.available2021-09-11T15:35:53Z-
dc.date.issued2009en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2008.12.019-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6334-
dc.description.abstractThis paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals. (C) 2008 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.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_US
dc.subjectFuzzy logicen_US
dc.subjectLyapunov exponenten_US
dc.subjectElectroencephalogram (EEG) signalsen_US
dc.titleAutomatic Detection of Electroencephalographic Changes Using Adaptive Neuro-Fuzzy Inference System Employing Lyapunov Exponentsen_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.volume36en_US
dc.identifier.issue5en_US
dc.identifier.startpage9031en_US
dc.identifier.endpage9038en_US
dc.identifier.wosWOS:000264782800036en_US
dc.identifier.scopus2-s2.0-60849087328en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1016/j.eswa.2008.12.019-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
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
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

29
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

23
checked on Aug 31, 2024

Page view(s)

64
checked on Dec 23, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.