Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6514
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Güler, İnan | - |
dc.contributor.author | Übeyli, Derya Elif | - |
dc.date.accessioned | 2021-09-11T15:37:03Z | - |
dc.date.available | 2021-09-11T15:37:03Z | - |
dc.date.issued | 2004 | en_US |
dc.identifier.issn | 1350-4533 | - |
dc.identifier.uri | https://doi.org/10.1016/j.medengphy.2004.06.007 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6514 | - |
dc.description.abstract | The new method presented in this study was directly based oil 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. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for detecting variabilities such as stenosis and occlusion in the physical state of internal carotid arterial Doppler signals. The computed Lyapunov exponents of the internal carotid arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The internal carotid arterial Doppler signals were classified with the accuracy varying from 94.87% to 97.44%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis and occlusion in internal carotid arteries. (C) 2004 IPEM. Published by Elsevier Ltd. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Sci Ltd | en_US |
dc.relation.ispartof | Medical Engineering & Physics | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Doppler signals | en_US |
dc.subject | internal carotid artery stenosis | en_US |
dc.subject | internal carotid artery occlusion | en_US |
dc.subject | chaotic signal | en_US |
dc.subject | Lyapunov exponents | en_US |
dc.subject | multilayer perceptron neural network (MLPNN) | en_US |
dc.subject | Levenberg-Marquardt algorithm | en_US |
dc.title | Detecting Variability of Internal Carotid Arterial Doppler Signals by Lyapunov Exponents | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 26 | en_US |
dc.identifier.issue | 9 | en_US |
dc.identifier.startpage | 763 | en_US |
dc.identifier.endpage | 771 | en_US |
dc.identifier.wos | WOS:000225749600008 | en_US |
dc.identifier.scopus | 2-s2.0-9644262694 | en_US |
dc.institutionauthor | Übeyli, Elif Derya | - |
dc.identifier.pmid | 15564113 | en_US |
dc.identifier.doi | 10.1016/j.medengphy.2004.06.007 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
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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