TY  - JOUR
T1  - Classification of EEG Signals in Depressed Patients
AU  - Eraldemir, Server Göksel
AU  - Kılıç, Ümit
AU  - Kaya Keleş, Mümine
AU  - Demirkol, Mehmet Emin
AU  - Yıldırım, Esen
AU  - Tamam, Lut
PY  - 2020
DA  - January
DO  - 10.17694/bajece.631951
JF  - Balkan Journal of Electrical and Computer Engineering
PB  - MUSA YILMAZ
WT  - DergiPark
SN  - 2147-284X
SP  - 103
EP  - 107
VL  - 8
IS  - 1
LA  - en
AB  - Electroencephalography(EEG) are electrical signals that occur in every activity of the brain.Investigation of normal and abnormal changes that take place in the human brainusing EEG signals is a widely used method in recent years. The World HealthOrganization (WHO) states that one of the most important health problems intoday's society is depressive disorders. Nowadays, various scales are used inthe diagnosis of depressive disorder in individuals. These scales are based onthe declaration of the individual. In recent studies, EEG has been used as abiomarker for the diagnosis of depression. In this study, EEG signals from 30patients with clinical depressive disorder have been recorded. EEG signals havebeen collected for 1 minute with eyes open and closed. The collected data havebeen divided into attributes by continuous wavelet transform which is used inmany studies in processing non-stationary signals such as EEG. Obtainedattributes have been classified with kNN classification method. As a result, itwas observed that EEG signals, collected from subjects with depression whileeyes are open and closed, can be classified with an accuracy of 91.30%.
KW  - EEG classification
KW  - depressive disorders
KW  - k-Nearest Neighbor (kNN) algorithm
KW  - Continuous Wavelet Transform
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UR  - https://doi.org/10.17694/bajece.631951
L1  - https://dergipark.org.tr/en/download/article-file/971527
ER  -