Complex-valued wavelet artificial neural network for Doppler signals classifying

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dc.contributor.authorOzbay, Yuksel
dc.contributor.authorKara, Sadik
dc.contributor.authorLatifoglu, Fatma
dc.contributor.authorCeylan, Rahime
dc.contributor.authorCeylan, Murat
dc.date.accessioned2020-03-26T17:17:08Z
dc.date.available2020-03-26T17:17:08Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractObjective: In this paper, the new complex-valued wavelet artificial neural network (CVWANN) was proposed for classifying Doppler signals recorded from patients and healthy volunteers. CVWANN was implemented on four different structures (CVWANN-1, -2, -3 and -4). Materials and methods: In this study, carotid arterial 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. In implemented structures in this paper, Haar wavelet and Mexican hat wavelet functions were used as real and imaginary parts of activation function on different sequence in hidden layer nodes. CVWANN-1, -2 -3 and -4 were implemented by using Haar-Haar, Mexican hat-Mexican hat, Haar-Mexican hat, Mexican hat-Haar as real-imaginary parts of activation function in hidden layer nodes, respectively. Results and conclusion: In contrast to CVWANN-2, which reached classification rates of 24.5%, CVWANN-1, -3 and -4 classified 40 healthy and 38 unhealthy subjects for both training and test phases with 100% correct classification rate using leave-one-out cross-validation. These networks have 100% sensitivity, 100% specifity and average detection rate is calculated as 100%. In addition, positive predictive value and negative predictive value were obtained as 100% for these networks. These results shown that CVWANN-1, -3 and -4 succeeded to classify Doppler signals. Moreover, training time and processing complexity were decreased considerable amount by using CVWANN-3. As conclusion, using of Mexican hat wavelet function in real and imaginary parts of hidden Layer activation function (CVWANN-2) is not suitable for classifying healthy and unhealthy subjects with high accuracy rate. The cause of unsuitability (obtaining the poor results in CVWANN-2) is tack of harmony between type of activation function in hidden layer and type of input signals in neural network. (c) 2007 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.artmed.2007.02.001en_US
dc.identifier.endpage156en_US
dc.identifier.issn0933-3657en_US
dc.identifier.issue2en_US
dc.identifier.pmid17400432en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage143en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.artmed.2007.02.001
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21281
dc.identifier.volume40en_US
dc.identifier.wosWOS:000247433200006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.ispartofARTIFICIAL INTELLIGENCE IN MEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectcomplex-valued wavelet artificialen_US
dc.subjectneural networken_US
dc.subjectwavelet neural networken_US
dc.subjectatherosclerosisen_US
dc.subjectcarotid arteryen_US
dc.subjectDoppler signalsen_US
dc.subjectleave-one-out cross-validationen_US
dc.titleComplex-valued wavelet artificial neural network for Doppler signals classifyingen_US
dc.typeArticleen_US

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