A New Approach Based on Discrete Hidden Markov Model Using Rocchio Algorithm for the Diagnosis of the Brain Diseases

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dc.contributor.authorUğuz, Harun
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T17:46:42Z
dc.date.available2020-03-26T17:46:42Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractTranscranial Doppler (TCD) study of the adult intracerebral circulation has gained an important popularity in last 10 years, since it is a non-invasive, easy to apply and reliable technique. In this study, an implementation on biomedical system has been developed for classification of signals gathered from middle cerebral arteries in the temporal area via TCD for 24 healthy and 82 ill people which have one of the four different brain patients such as; cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. Basically, the system is composed of feature extraction and classification parts. In the feature extraction stage, the Linear Predictive Coding (LPC) Analysis and Cepstral Analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage a new Discrete Hidden Markov Model (DHMM) based approach was proposed for the diagnosis of brain diseases. This proposed method was developed via Rocchio algorithm. Therefore, to calculate DHMM parameters regulated according to maximum likelihood (ML) approach, both training samples of related class and other classes were included in calculation. Thus, DHMM model parameters presenting one class were suggested to represent the training samples related to that class better as well as not to represent the training samples related to other classes. The performance of the proposed DHMM with Rocchio approach was compared with some methods such as DHMM, Artificial Neural Network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods.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.citationUğuz, H., Arslan, A., (2010). A New Approach Based on Discrete Hidden Markov Model Using Rocchio Algorithm for the Diagnosis of the Brain Diseases. Digital Signal Processing, 20(3), 923-934. Doi: 10.1016/j.dsp.2009.11.001
dc.identifier.doi10.1016/j.dsp.2009.11.001en_US
dc.identifier.endpage934en_US
dc.identifier.issn1051-2004en_US
dc.identifier.issn1095-4333en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage923en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.dsp.2009.11.001
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24523
dc.identifier.volume20en_US
dc.identifier.wosWOS:000276289800029en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorUğuz, Harun
dc.institutionauthorArslan, Ahmet
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.ispartofDigital Signal Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectTranscranial doppler signalsen_US
dc.subjectDiscrete hidden markov modelen_US
dc.subjectRocchio algorithmen_US
dc.subjectMaximum likelihooden_US
dc.titleA New Approach Based on Discrete Hidden Markov Model Using Rocchio Algorithm for the Diagnosis of the Brain Diseasesen_US
dc.typeArticleen_US

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