Izci, ElifOzdemir, Mehmet AkifAkan, AydinOzcoban, Mehmet AkifArikan, Mehmet Kemal2023-06-162023-06-162021978-1-6654-3649-6https://doi.org/10.1109/SIU53274.2021.9477800https://hdl.handle.net/20.500.14365/196829th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORKMajor 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.trinfo:eu-repo/semantics/closedAccessDepressionElectroencephalographySignal processingClassificationConference Object10.1109/SIU53274.2021.94778002-s2.0-85111447341