Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

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dc.contributor.authorTagluk, M. Emin
dc.contributor.authorSezgin, Necmettin
dc.contributor.authorAkin, Mehmet
dc.date.accessioned2024-04-24T16:02:15Z
dc.date.available2024-04-24T16:02:15Z
dc.date.issued2010
dc.departmentDicle Üniversitesien_US
dc.description.abstractAnalysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.en_US
dc.identifier.doi10.1007/s10916-009-9286-5
dc.identifier.endpage725en_US
dc.identifier.issn0148-5598
dc.identifier.issn1573-689X
dc.identifier.issue4en_US
dc.identifier.pmid20703927
dc.identifier.scopus2-s2.0-77956063187
dc.identifier.scopusqualityQ1
dc.identifier.startpage717en_US
dc.identifier.urihttps://doi.org/10.1007/s10916-009-9286-5
dc.identifier.urihttps://hdl.handle.net/11468/14712
dc.identifier.volume34en_US
dc.identifier.wosWOS:000280071200032
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Medical Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEegen_US
dc.subjectEmgen_US
dc.subjectEogen_US
dc.subjectSleep Stagesen_US
dc.subjectAnnen_US
dc.titleEstimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOGen_US
dc.titleEstimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG
dc.typeArticleen_US

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