Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5617
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dc.contributor.authorGüler N. F.-
dc.contributor.authorÜbeyli, Elif Derya-
dc.contributor.authorGüler, İnan-
dc.date.accessioned2021-09-11T15:19:24Z-
dc.date.available2021-09-11T15:19:24Z-
dc.date.issued2005en_US
dc.identifier.citation2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, 1 September 2005 through 4 September 2005, Shanghai, 69123en_US
dc.identifier.isbn0780387406; 9780780387409-
dc.identifier.issn0589-1019-
dc.identifier.urihttps://doi.org/10.1109/iembs.2005.1615485-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5617-
dc.description.abstractThis paper illustrates the use of combined neural network models to guide model selection for diagnosis of internal carotid arterial disorders. The method presented in this study was directly based on the consideration that internal carotid arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Statistics were used over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. The first level networks were implemented for the diagnosis of internal carotid arterial disorders using the selected Lyapunov exponents as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChaotic signalen_US
dc.subjectCombined neural networken_US
dc.subjectDoppler signalen_US
dc.subjectInternal carotid arteryen_US
dc.subjectLyapunov exponentsen_US
dc.titleCombined Neural Network Model Employing Lyapunov Exponents: Internal Carotid Arterial Disorders Detection Caseen_US
dc.typeConference Objecten_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.volume7 VOLSen_US
dc.identifier.startpage4564en_US
dc.identifier.endpage4567en_US
dc.identifier.scopus2-s2.0-33846904562en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1109/iembs.2005.1615485-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005en_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
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
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