Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/1968
Title: | An Eeg and Machine Learning Based Method for the Detection of Major Depressive Disorder |
Other Titles: | Majör Depresif Bozuklu?un Tespiti için Eeg ve Makine Ö?renmesi Tabanli Bir Yöntem |
Authors: | Izci, Elif Ozdemir, Mehmet Akif Akan, Aydin Ozcoban, Mehmet Akif Arikan, Mehmet Kemal |
Keywords: | Depression Electroencephalography Signal processing Classification |
Publisher: | IEEE |
Abstract: | Major 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. |
Description: | 29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK |
URI: | https://doi.org/10.1109/SIU53274.2021.9477800 https://hdl.handle.net/20.500.14365/1968 |
ISBN: | 978-1-6654-3649-6 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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