Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6706
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dc.contributor.authorÜbeyli, Elif Derya-
dc.contributor.authorGüler, İnan-
dc.date.accessioned2021-09-11T15:43:15Z-
dc.date.available2021-09-11T15:43:15Z-
dc.date.issued2005en_US
dc.identifier.issn0010-4825-
dc.identifier.issn1879-0534-
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2004.06.006-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6706-
dc.description.abstractThis paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. 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. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases. (c) 2004 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.subjectfeature extractionen_US
dc.subjectautomated diagnosisen_US
dc.subjectDoppler signalen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectophthalmic arteryen_US
dc.subjectinternal carotid arteryen_US
dc.titleFeature extraction from Doppler ultrasound signals for automated diagnostic systemsen_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.volume35en_US
dc.identifier.issue9en_US
dc.identifier.startpage735en_US
dc.identifier.endpage764en_US
dc.identifier.wosWOS:000233689600001en_US
dc.identifier.scopus2-s2.0-27744513132en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.pmid16278106en_US
dc.identifier.doi10.1016/j.compbiomed.2004.06.006-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
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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