Classification of transcranial Doppler signals using their chaotic invariant measures

Basit Öğe Kaydı
dc.contributor.authorOzturk, Ali
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T17:17:07Z
dc.date.available2020-03-26T17:17:07Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, chaos analysis was performed on the transcranial Doppler (TCD) signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. Two chaotic invariant measures, i.e. the maximum Lyapunov exponent and the correlation dimension, were calculated for the TCD signals after applying nonlinearity and stationarity tests to them. The sonograms obtained via Burg autoregressive (AR) method demonstrated that the chaotic invariant measures represented the unpredictability and complexity levels of the TCD signals. According to the multiple linear regression analysis, the chaotic invariant measures were found to be highly significant for the regression equation which fitted to the data. This result suggested that the chaotic invariant measures could be used for automatically differentiating various cerebrovascular conditions via an appropriate classifier. For comparison purposes, we investigated several different classification algorithms. The k-nearest neighbour algorithm outperformed all the other classifiers with a classification accuracy of 94.44% on the test data. We used the receiver operating characteristic (ROC) curves in order to assess the performance of the classifiers. The results suggested that the classification systems which use the chaotic invariant measures as input have potential in detecting the blood flow velocity changes due to various brain diseases. (c) 2007 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2007.02.004en_US
dc.identifier.endpage180en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issn1872-7565en_US
dc.identifier.issue2en_US
dc.identifier.pmid17386958en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage171en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2007.02.004
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21270
dc.identifier.volume86en_US
dc.identifier.wosWOS:000246266300008en_US
dc.identifier.wosqualityQ2en_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.subjecttranscranial Doppler signalsen_US
dc.subjectnonlinear analysisen_US
dc.subjectchaotic measuresen_US
dc.subjectclassificationen_US
dc.subjectNEFCLASSen_US
dc.subjectdecision treesen_US
dc.subjectK-nearest neighbouren_US
dc.subjectmultilayer perceptronen_US
dc.titleClassification of transcranial Doppler signals using their chaotic invariant measuresen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

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