Automated elimination of EOG artifacts in sleep EEG using regression method

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dc.contributor.authorDursun, Mehmet
dc.contributor.authorOzsen, Seral
dc.contributor.authorGunes, Salih
dc.contributor.authorAkdemir, Bayram
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2024-02-23T14:37:20Z
dc.date.available2024-02-23T14:37:20Z
dc.date.issued2019
dc.departmentNEÜen_US
dc.description.abstractSleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%-1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [113E591]; Scientific Research Projects Coordination Unit of Konya Technical Universityen_US
dc.description.sponsorshipThis study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) (project no. 113E591) and The Scientific Research Projects Coordination Unit of Konya Technical University.en_US
dc.identifier.doi10.3906/elk-1809-180
dc.identifier.endpage1108en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85065839441en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1094en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1809-180
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16056
dc.identifier.volume27en_US
dc.identifier.wosWOS:000463355800031en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectElectrooculogram Artifact Eliminationen_US
dc.subjectRegressionen_US
dc.subjectSleep Stage Scoringen_US
dc.titleAutomated elimination of EOG artifacts in sleep EEG using regression methoden_US
dc.typeArticleen_US

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