A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

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dc.contributor.authorDursun, Mehmet
dc.contributor.authorOzsen, Seral
dc.contributor.authorYucelbas, Cuneyt
dc.contributor.authorYucelbas, Sule
dc.contributor.authorTezel, Gulay
dc.contributor.authorKuccukturk, Serkan
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2024-02-23T13:55:50Z
dc.date.available2024-02-23T13:55:50Z
dc.date.issued2017
dc.departmentNEÜen_US
dc.description.abstractInterference between EEG and EOG signals has been studied heavily in clinical EEG signal processing applications. But, in automatic sleep stage classification studies these effects are generally ignored. Thus, the objective of this study was to eliminate EOG artifacts from the EEG signals and to see the effects of this process. We proposed a new scheme in which EOG artifacts are separated from electrode or other line artifacts by a correlation and discrete wavelet transform-based rule. Also, to discriminate the situation of EEG contamination to EOG from EOG contamination to EEG, we introduced another rule and integrated this rule to our proposed method. The proposed method was also evaluated under two different circumstances: EOG-EEG elimination along the whole 0.3-35 Hz power spectrum and EOG-EEG elimination with discrete wavelet transform in 0-4 Hz frequency range. To see the consequences of EOG-EEG elimination in these circumstances, we classified pure EEG and artifact-eliminated EEG signals for each situation with artificial neural networks. The results on 11 subjects showed that pure EEG signals gave a mean classification accuracy of 60.12 %. The proposed EOG elimination process performed in 0-35 Hz frequency range resulted in a classification accuracy of 63.75 %. Furthermore, conducting EOG elimination process by using 0-4 Hz DWT detail coefficients caused this accuracy to be raised to 68.15 %. By comparing the results obtained from all applications, we concluded that an improvement about 8.03 % in classification accuracy with regard to the uncleaned EEG signals was achieved.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey [113E591]; Scientific Research Projects Coordination Unit of Selcuk Universityen_US
dc.description.sponsorshipThis study is supported by the Scientific and Technological Research Council of Turkey (Project No. 113E591) and the Scientific Research Projects Coordination Unit of Selcuk University.en_US
dc.identifier.doi10.1007/s00521-016-2578-z
dc.identifier.endpage3112en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-84986266793en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3095en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2578-z
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10978
dc.identifier.volume28en_US
dc.identifier.wosWOS:000426865100020en_US
dc.identifier.wosqualityQ1en_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.subjectSleep Eegen_US
dc.subjectEog Artifact Eliminationen_US
dc.subjectSleep Stage Scoringen_US
dc.subjectArtificial Neural Networksen_US
dc.titleA new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classificationen_US
dc.typeArticleen_US

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