Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

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dc.contributor.authorUguz, Harun
dc.date.accessioned2020-03-26T18:24:40Z
dc.date.available2020-03-26T18:24:40Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractDoppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decades, because it is a non-invasive, easy to apply and reliable technique. In this study, a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) has been developed in order to classify the internal carotid artery Doppler signals obtained from the 191 subjects, 136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subject. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, power spectral density (PSD) estimates of internal carotid artery Doppler signals were obtained by using Burg autoregressive (AR) spectrum analysis technique in order to obtain medical information. In the classification stage, LVQ NN was used classify features from Burg AR method. In experiments, LVQ NN based method reached 97.91% classification accuracy with 5 fold Cross Validation (CV) technique. In addition, the classification performance of the LVQ NN was compared with some methods such as Multi Layer Perceptron (MLP) NN, Naive Bayes (NB), K-Nearest Neighbor (KNN), decision tree and Support Vector Machine (SVM) with sensitivity and specificity statistical parameters. The classification results showed that the LVQ NN method is effective for classification of internal carotid artery Doppler signals.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThe authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects.en_US
dc.identifier.doi10.1007/s10916-010-9498-8en_US
dc.identifier.endpage540en_US
dc.identifier.issn0148-5598en_US
dc.identifier.issue2en_US
dc.identifier.pmid20703698en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage533en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s10916-010-9498-8
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27886
dc.identifier.volume36en_US
dc.identifier.wosWOS:000303825500019en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF MEDICAL SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectLearning vector quantizationen_US
dc.subjectDoppler signalen_US
dc.subjectCarotid arteryen_US
dc.subjectPower spectral densityen_US
dc.subjectAutoregressive methoden_US
dc.titleDetection of Carotid Artery Disease by Using Learning Vector Quantization Neural Networken_US
dc.typeArticleen_US

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