Detection of REM in sleep EOG signals

Basit Öğe Kaydı
dc.contributor.authorCoskun A.
dc.contributor.authorOzsen S.
dc.contributor.authorYucelbas S.
dc.contributor.authorYucelbas C.
dc.contributor.authorTezel G.
dc.contributor.authorKuccukturk S.
dc.contributor.authorYosunkaya S.
dc.date.accessioned2020-03-26T19:31:58Z
dc.date.available2020-03-26T19:31:58Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBackground/Objectives: Sleep staging is very important phase for diagnosing respiration and sleep diseases. Nowadays, Electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG) signals are particularly used together in studies on sleep staging. Methods/Statistical Analysis: Associating only EOG signals to sleep staging was distinctly purposed. So, this paper deals with extraction features and classifying for determining REM-NREM states from the EOG signals. In this study, left eye (LEOG) and the right eye (REOG) signals were used. After EOG signals were obtained, 21 different features were extracted from LEOG and REOG in time and frequency domain according to rules of American Academy of Sleep Medicine (AASM). Findings: Artificial Neural Networks (ANN) was adopted on features as method of classification with 3-fold cross validation technique and reached conclusion with the maximum test classification accuracy as 88.05%. To obtain higher classification accuracies, Sequential Backward Selection (SBS) method was used. According to results of SBS, number of the best features combination was determined as 13 and the maximum classification accuracy was obtained as 89.62%. The optimum value of hidden layer node number of ANN was determined as 15 for the best features. Application/Improvements: When looking from the viewpoint of percentage of classification accuracy of this study, a result can be seen that is non-negligible value for literature.en_US
dc.identifier.doi10.17485/ijst/2016/v9i25/97224en_US
dc.identifier.issn0974-6846en_US
dc.identifier.issue25en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.17485/ijst/2016/v9i25/97224
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34264
dc.identifier.volume9en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIndian Society for Education and Environmenten_US
dc.relation.ispartofIndian Journal of Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networksen_US
dc.subjectEOGen_US
dc.subjectFeature selectionen_US
dc.subjectSBSen_US
dc.subjectSleep stageen_US
dc.titleDetection of REM in sleep EOG signalsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu