Ensemble Approach for Detection of Depression Using EEG Features |
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| Authors: | Egils Avots Kl&#x;vs Jermakovs Maie Bachmann Laura Peske Cagri Ozcinar Gholamreza Anbarjafari |
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| Institution: | 1.iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia; (E.A.); (K.J.); (C.O.);2.Biosignal Processing Laboratory, Tallinn University of Technology, 19086 Tallinn, Estonia; (M.B.); (L.P.);3.PwC Advisory, 00180 Helsinki, Finland;4.Faculty of Egineering, Hasan Kalyoncu University, 27000 Gaziantep, Turkey |
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| Abstract: | Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression. |
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| Keywords: | depression electroencephalogram (EEG) feature extraction and selection machine learning ensemble learning |
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