A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals

Basit Öğe Kaydı
dc.contributor.authorUguz, Harun
dc.date.accessioned2020-03-26T18:23:31Z
dc.date.available2020-03-26T18:23:31Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractA transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage. (C) 2011 Elsevier Ireland Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThe author acknowledges the support of this study provided by Selcuk University Scientific Research Projects. Also, the author thanks Dr. Firat Hardalac for providing the TCD signals.en_US
dc.identifier.doi10.1016/j.cmpb.2011.03.013en_US
dc.identifier.endpage609en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issn1872-7565en_US
dc.identifier.issue3en_US
dc.identifier.pmid21524813en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage598en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2011.03.013
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27670
dc.identifier.volume107en_US
dc.identifier.wosWOS:000307093400021en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherELSEVIER IRELAND LTDen_US
dc.relation.ispartofCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectFeature selectionen_US
dc.subjectPrincipal component analysisen_US
dc.subjectInformation gainen_US
dc.subjectDiscrete wavelet transformen_US
dc.subjectSupport vector machineen_US
dc.titleA hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signalsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu