The ANN-based computing of drowsy level

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dc.contributor.authorKurt, Muhammed B.
dc.contributor.authorSezgin, Necmettin
dc.contributor.authorAkin, Mehmet
dc.contributor.authorKirbas, Gokhan
dc.contributor.authorBayram, Muhittin
dc.date.accessioned2024-04-24T16:11:24Z
dc.date.available2024-04-24T16:11:24Z
dc.date.issued2009
dc.departmentDicle Üniversitesien_US
dc.description.abstractWe have developed a new method for automatic estimation of vigilance level by using electroencephalogram (EEG), electromyogram (EMG) and eye movement (EOG) signals recorded during transition from wakefulness to sleep. In the previous studies, EEG signals and EEG signals with EMG signals were used for estimating vigilance levels. In the present study, it was aimed to estimate vigilance levels by using EEG, EMG and EOG signals. The changes in EEG, EMG and EOG were diagnosed while transiting from wakefulness to sleep by using wavelet transform and developed artificial neural network (ANN). EEG signals were separated to its subbands using wavelet transform, LEOG (Left EOG), REOG (Right EOG) and chin EMG was used in ANN process for increasing the accuracy of the estimation rate by evaluating their tonic levels and also used in data preparation stage to verify and eliminate the movement artifacts. Then, training and testing data sets consist of the EEG subbands (delta, theta, alpha and beta); LEOG, REOG and EMG signals were applied to the ANN for training and testing the system which gives three Situations for the vigilance level of the subject: Awake, drowsy, and sleep. The accuracy of estimation is about 97-98% while the accuracy of the previous study which used only EEG was 95-96% and the study which used EEG with EMG was 98-99%. The reason of decreasing the percentage of present study according to the last study is because of the increase of the input data. (C) 2008 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2008.01.085
dc.identifier.endpage2542en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-56349136853
dc.identifier.scopusqualityQ1
dc.identifier.startpage2534en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2008.01.085
dc.identifier.urihttps://hdl.handle.net/11468/15381
dc.identifier.volume36en_US
dc.identifier.wosWOS:000262178000151
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applications
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.subjectWaveleten_US
dc.subjectNeural Networksen_US
dc.subjectDrowsyen_US
dc.subjectSleepen_US
dc.titleThe ANN-based computing of drowsy levelen_US
dc.titleThe ANN-based computing of drowsy level
dc.typeArticleen_US

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