Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

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dc.contributor.authorOzbay, Yuksel
dc.contributor.authorCeylan, Murat
dc.date.accessioned2020-03-26T17:17:23Z
dc.date.available2020-03-26T17:17:23Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study. carotid artery Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Doppler signals were processed using fast Fourier transform (FFT) with different window types, Hilbert transform and Welch methods. After these processes. Doppler signals were classified using complex-valued artificial neural network (CVANN). Effects of window types in classification were interpreted. Results for three methods and five window types (Bartlett, Blackman, Boxcar, Hamming, Harming) were presented as comparatively. CVANN is a new technique for solving classification problems in Doppler signals. Furthermore, examining the effects of window types in addition to CVANN in this classification problem is also the first study in literature related with this subject. Results showed that CVANN, whose input data were processed by Welch method for each window types stated above, had classified all training and test patterns, which consist of 36 healthy, 34 unhealthy and four healthy, four unhealthy subjects, respectively, with 100% classification accuracy for both training and test phases. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compbiomed.2006.01.008en_US
dc.identifier.endpage295en_US
dc.identifier.issn0010-4825en_US
dc.identifier.issue3en_US
dc.identifier.pmid16603148en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage287en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compbiomed.2006.01.008
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21375
dc.identifier.volume37en_US
dc.identifier.wosWOS:000244357600003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofCOMPUTERS IN BIOLOGY AND MEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectatherosclerosisen_US
dc.subjectcarotid arteryen_US
dc.subjectDoppler signalsen_US
dc.subjectcomplex valued ANNen_US
dc.subjectwindow typesen_US
dc.subjectFFTen_US
dc.subjectHilbert transformen_US
dc.subjectWelch transformen_US
dc.titleEffects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural networken_US
dc.typeArticleen_US

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