Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7529
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dc.contributor.authorÜbeyli, Elif Derya-
dc.contributor.authorGüler, Inan-
dc.date.accessioned2021-09-11T15:57:37Z-
dc.date.available2021-09-11T15:57:37Z-
dc.date.issued2005en_US
dc.identifier.citationConference of the World-Academy-of-Science-Engineering-and-Technology -- JAN 19-21, 2005 -- Berlin, GERMANYen_US
dc.identifier.issn1307-6884-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7529-
dc.description.abstractA new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephaloggraphic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. 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 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.en_US
dc.language.isoenen_US
dc.publisherWorld Acad Sci, Eng & Tech-Waseten_US
dc.relation.ispartofProceedings of World Academy of Science, Engineering And Technology, Vol 2en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChaotic signalen_US
dc.subjectElectroencephalogram (EEG) signalsen_US
dc.subjectFeature extraction/selectionen_US
dc.subjectLyapunov exponentsen_US
dc.titleStatistics over Lyapunov Exponents for Feature Extraction: Electroencephalographic Changes Detection Caseen_US
dc.typeConference Objecten_US
dc.relation.ispartofseriesProceedings of World Academy of Science Engineering and Technologyen_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.volume2en_US
dc.identifier.startpage129en_US
dc.identifier.endpage132en_US
dc.identifier.wosWOS:000259631300033en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conferenceConference of the World-Academy-of-Science-Engineering-and-Technologyen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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