Detection of Depression and Scaling of Severity Using Six Channel EEG Data

Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling of depression. Any degradation in the working of the brain can be identified through change in the electroencephalogram (EEG) signal. Thus detection as well as severity scaling of depression is done in this study using EEG signal. In this study, features are extracted from the temporal region of the brain using six (FT7, FT8, T7, T8, TP7, TP8) channels. The linear features used are delta, theta, alpha, beta, gamma1 and gamma2 band power and their corresponding asymmetry as well as paired asymmetry. The non-linear features used are Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA). The classifiers used are: Bagging along with three different kernel functions (Polynomial, Gaussian and Sigmoidal) of Support Vector Machine (SVM). Feature selection technique used is ReliefF. Highest classification accuracy of 96.02% and 79.19% was achieved for detection and severity scaling of depression using SVM (Gaussian Kernel Function) and ReliefF as feature selection. From the analysis, it was found that depression affects the temporal region of the brain (temporo-parietal region).It was also found that depression affects the higher frequency band features more and it affects each hemisphere differently. It can also be analysed that out of all the kernel of SVM, Gaussian kernel is more efficient to other kernels. Of all the features, combination of all paired asymmetry and asymmetry showed high classification accuracy (accuracy of 90.26% for detection of depression and accuracy of 75.31% for severity scaling).