Detection of major depressive disorder using linear and non-linear features from EEG signals

Abstract

EEG signals are non-stationary, complex and non-linear signals. During major depressive disorder (MDD) or depression, any deterioration in the brain function is reflected in the EEG signals. In this paper, linear features (band power, inter hemispheric asymmetry) and non-linear features [relative wavelet energy (RWE) and wavelet entropy (WE)] and combination of linear and non-linear features were used to classify depression patients and healthy individuals. In this analysis the data set used is publicly available data set contributed by Mumtaz et al. (Biomed Signal Process Control 31:108–115, 2017b). The dataset consisted of 34 MDD patients and 30 healthy individuals. The classifiers used were multi layered perceptron neural network (MLPNN), radial basis function network (RBFN), linear discriminant analysis (LDA) and quadratic discriminant analysis. When linear feature was used, highest classification accuracy of 91.67% was obtained by alpha power with MLPNN classifier. When non-linear feature was used, both RWE and WE provided highest classification accuracy of 90% with RBFN and LDA classifier, respectively. The highest classification of 93.33% was achieved when combining linear and non-linear feature, i.e., combination alpha power and RWE with MLPNN as well as RBFN classifier. This paper also showed that the combination of non-linear features, i.e., RWE and WE also performed the best with highest classification accuracy of 93.33%. The study compared the accuracy, sensitivity and specificity of different classifiers along with linear and non-linear features and combination of both. The results indicated that combination alpha power and RWE showed the highest classification 93.33% accuracy in all the applied classifiers.