Classification of EEG Signals in Depressed Patients

Year 2020, , 103 - 107, 31.01.2020

Server Göksel Eraldemir , Ümit Kılıç Mümine Kaya Keleş Mehmet Emin Demirkol , Esen Yıldırım , Lut Tamam

https://doi.org/10.17694/bajece.631951

Cited By: 3

Abstract

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 brain
using EEG signals is a widely used method in recent years. The World Health
Organization (WHO) states that one of the most important health problems in
today's society is depressive disorders. Nowadays, various scales are used in
the diagnosis of depressive disorder in individuals. These scales are based on
the declaration of the individual. In recent studies, EEG has been used as a
biomarker for the diagnosis of depression. In this study, EEG signals from 30
patients with clinical depressive disorder have been recorded. EEG signals have
been collected for 1 minute with eyes open and closed. The collected data have
been divided into attributes by continuous wavelet transform which is used in
many studies in processing non-stationary signals such as EEG. Obtained
attributes have been classified with kNN classification method. As a result, it
was observed that EEG signals, collected from subjects with depression while
eyes are open and closed, can be classified with an accuracy of 91.30%.

Keywords

EEG classification, depressive disorders, k-Nearest Neighbor (kNN) algorithm, Continuous Wavelet Transform

Supporting Institution

Adana Alparslan Türkeş Science and Technology University

Project Number

18103031

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