Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/7526
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Übeyli, Elif Derya | - |
dc.date.accessioned | 2021-09-11T15:57:36Z | - |
dc.date.available | 2021-09-11T15:57:36Z | - |
dc.date.issued | 2008 | en_US |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.issn | 1879-0534 | - |
dc.identifier.uri | https://doi.org/10.1016/j.compbiomed.2007.12.002 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7526 | - |
dc.description.abstract | The objective of the present study is to extract the representative features of the internal carotid arterial (ICA) Doppler ultrasound signals and to present the accurate classification model. This paper presented the usage of statistics over the set of the extracted features (Lyapunov exponents and the power levels of the power spectral density estimates obtained by the eigenvector methods) 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. Mixture of experts (ME) and modified mixture of experts (MME) architectures were formulated and used as basis for detection of arterial disorders. Three types of ICA Doppler signals (Doppler signals recorded from healthy subjects, subjects having stenosis, and subjects having occlusion) were classified. The classification results confirmed that the proposed ME and MME has potential in detecting the arterial disorders. (C) 2007 Elsevier Ltd. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Pergamon-Elsevier Science Ltd | en_US |
dc.relation.ispartof | Computers In Biology And Medicine | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Doppler signals | en_US |
dc.subject | Lyapunov exponents | en_US |
dc.subject | eigenvector methods | en_US |
dc.subject | feature extraction/selection | en_US |
dc.subject | mixture of experts | en_US |
dc.subject | modified mixture of experts | en_US |
dc.title | Statistics over features for internal carotid arterial disorders detection | 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 | 38 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 361 | en_US |
dc.identifier.endpage | 371 | en_US |
dc.identifier.wos | WOS:000254733000008 | en_US |
dc.identifier.scopus | 2-s2.0-39549089504 | en_US |
dc.institutionauthor | Übeyli, Elif Derya | - |
dc.identifier.pmid | 18179791 | en_US |
dc.identifier.doi | 10.1016/j.compbiomed.2007.12.002 | - |
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 |
CORE Recommender
SCOPUSTM
Citations
8
checked on Dec 21, 2024
WEB OF SCIENCETM
Citations
8
checked on Dec 21, 2024
Page view(s)
80
checked on Dec 23, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.