Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

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dc.contributor.authorUguz, Harun
dc.contributor.authorGuraksin, Gur Emre
dc.contributor.authorErgun, Ucman
dc.contributor.authorSaracoglu, Ridvan
dc.date.accessioned2020-03-26T18:13:52Z
dc.date.available2020-03-26T18:13:52Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractWhen the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals. (C) 2010 Elsevier Ireland Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk University; Afyon Kocatepe UniversityAfyon Kocatepe Universityen_US
dc.description.sponsorshipThe authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects. Also, this study has been supported by Scientific Research Project of Afyon Kocatepe University.en_US
dc.identifier.doi10.1016/j.cmpb.2010.07.001en_US
dc.identifier.endpage60en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issue1en_US
dc.identifier.pmid20673596en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage51en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2010.07.001
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26199
dc.identifier.volume103en_US
dc.identifier.wosWOS:000292523600005en_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.subjectDiscrete Hidden Markov Modelen_US
dc.subjectDoppler signalen_US
dc.subjectCarotid arteryen_US
dc.subjectRocchio algorithmen_US
dc.subjectGenetic algorithmen_US
dc.titleBiomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signalsen_US
dc.typeArticleen_US

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