Identification of Major Depressive Disorder: Using Significant Features of EEG Signals Obtained by Random Forest and Ant Colony Optimization Methods

Electroencephalogram (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings. It is also used to diagnose sleep disorders, depth of anesthesia, coma, encephalopathy, brain death, and depression. Being one of the prevalent psychiatric disorders, depressive episodes of major depressive disorder (MDD) is often misdiagnosed or overlooked. Therefore, identifying MDD at earlier stages of treatment could help to facilitate efficient and specific treatment. In this article, Random Forest (RF) and Ant Colony Optimization (ACO) algorithm are used to reduce the number of features by removing irrelevant and redundant features. The selected features are then fed into k-nearest neighbors (KNN) and SVM classifiers, a mathematical tool for data classification, regression, function estimation, and modeling processes, in order to classify MDD and non-MDD subjects. The proposed method used Wavelet Transformation (WT) to decompose the EEG data into corresponding frequency bands, like delta, theta, alpha, beta and gamma. A total of 119 participants were recruited by the University of Arizona from introductory psychology classes based on survey scores of the Beck Depression Inventory (BDI). The performance of KNN and SVM classifiers is measured first with all the features and then with selected significant features given by RF and ACO. It is possible to discriminate 44 MDD and 75 non-MDD subjects efficiently using 15 of 65 channels and 3 of 5 frequency bands to improve the performance, where the significant features are obtained by the RF method. It is found that the classification accuracy has been improved from70.21% and76.67% using all the features to the corresponding 91.67% and 83.33% with only significant features using KNN and Support Vector Machine (SVM) respectively.

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