Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7528
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dc.contributor.authorÜbeyli, Elif Derya-
dc.date.accessioned2021-09-11T15:57:37Z-
dc.date.available2021-09-11T15:57:37Z-
dc.date.issued2009en_US
dc.identifier.issn0010-4825-
dc.identifier.issn1879-0534-
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2009.06.001-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7528-
dc.description.abstractThis paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of 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) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes. (C) 2009 Elsevier Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers In Biology And Medicineen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectroencephalogram (EEG) signalsen_US
dc.subjectFeature extraction/selectionen_US
dc.subjectLyapunov exponentsen_US
dc.subjectWavelet coefficientsen_US
dc.subjectEigenvector methodsen_US
dc.titleStatistics Over Features: Eeg Signals Analysisen_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.volume39en_US
dc.identifier.issue8en_US
dc.identifier.startpage733en_US
dc.identifier.endpage741en_US
dc.identifier.wosWOS:000268430700009en_US
dc.identifier.scopus2-s2.0-67649601026en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.pmid19555931en_US
dc.identifier.doi10.1016/j.compbiomed.2009.06.001-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
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
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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