Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/1968
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dc.contributor.authorIzci, Elif-
dc.contributor.authorOzdemir, Mehmet Akif-
dc.contributor.authorAkan, Aydin-
dc.contributor.authorOzcoban, Mehmet Akif-
dc.contributor.authorArikan, Mehmet Kemal-
dc.date.accessioned2023-06-16T14:31:04Z-
dc.date.available2023-06-16T14:31:04Z-
dc.date.issued2021-
dc.identifier.isbn978-1-6654-3649-6-
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9477800-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/1968-
dc.description29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORKen_US
dc.description.abstractMajor depressive disorder (MDD) is a common mood disorder encountered worldwide. Early diagnosis has great importance to prevent the negative effects on the person. The aim of this study is to develop an objective method to differentiate MDD patients from healthy controls. Electroencephalography (EEG) signals taken from 16 MDD patients and 16 healthy subjects are analyzed according to the regions of the brain, and time-domain, frequency-domain, and nonlinear features were extracted. The feature sets are classified using five different classification algorithms. As a result of the study, a classification accuracy of 89.5% was yielded using the Bagging classifier with 7 features calculated from the central EEG channels.en_US
dc.description.sponsorshipIEEE,IEEE Turkey Secten_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof29Th Ieee Conference on Sıgnal Processıng And Communıcatıons Applıcatıons (Sıu 2021)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDepressionen_US
dc.subjectElectroencephalographyen_US
dc.subjectSignal processingen_US
dc.subjectClassificationen_US
dc.titleAn EEG and Machine Learning based Method for the Detection of Major Depressive Disorderen_US
dc.title.alternativeMajör depresif bozuklu?un tespiti için EEG ve makine ö?renmesi tabanli bir yöntemen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/SIU53274.2021.9477800-
dc.identifier.scopus2-s2.0-85111447341en_US
dc.departmentİzmir Ekonomi Üniversitesien_US
dc.authoridOzdemir, Mehmet Akif/0000-0002-8758-113X-
dc.authoridİzci, Elif/0000-0003-1148-8374-
dc.authorwosidOzdemir, Mehmet Akif/G-7952-2018-
dc.authorwosidİzci, Elif/GOE-6084-2022-
dc.authorscopusid57206467904-
dc.authorscopusid57206479576-
dc.authorscopusid35617283100-
dc.authorscopusid35181765100-
dc.authorscopusid26423493800-
dc.identifier.wosWOS:000808100700043en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1tr-
item.cerifentitytypePublications-
crisitem.author.dept05.06. Electrical and Electronics Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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