Classification of sleep stages using class-dependent sequential feature selection and artificial neural network

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dc.contributor.authorOzsen, Seral
dc.date.accessioned2020-03-26T18:41:18Z
dc.date.available2020-03-26T18:41:18Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSeveral studies have been conducted for automatic classification of sleep stages to ease time-consuming manual scoring process that can involve a high degree of experience and subjectivity. But none of them has found a practical usage in medical area so far because of their under acceptable success rates. In this study, a different classification scheme is proposed to increase the success rate in automatic sleep stage scoring in which sleep stages were classified as Awake, Non-REM1, Non-REM2, Non-REM3 and REM stages. Using EEG, EMG and EOG recordings of five healthy subjects, a modified version of sequential feature selection method was applied to the sleep epochs in class by class basis and different artificial neural network (ANN) architectures were trained for each class. That is to say, sleep stages were classified with five ANN architectures each of which uses different features and different network parameters for classification. The highest classification accuracy was obtained for REM sleep as 95.13 % in addition to the lowest classification accuracy of 86.42 % for Non-REM3 sleep. The overall accuracy, on the other hand, was recorded as 90.93 %, which is a comparatively good result when the other studies using all stages are taken into account.en_US
dc.description.sponsorshipScientific Research Projects of Selcuk UniversitySelcuk University [05401069]en_US
dc.description.sponsorshipThis study is supported by the Scientific Research Projects of Selcuk University (project no. 05401069).en_US
dc.identifier.doi10.1007/s00521-012-1065-4en_US
dc.identifier.endpage1250en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1239en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-012-1065-4
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29302
dc.identifier.volume23en_US
dc.identifier.wosWOS:000325026400004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectAutomatic sleep stage classificationen_US
dc.subjectSequential feature selectionen_US
dc.titleClassification of sleep stages using class-dependent sequential feature selection and artificial neural networken_US
dc.typeArticleen_US

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