Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification

In this paper, we propose an interpretable electroencephalogram (EEG)-based solution for the diagnostics of major depressive disorder (MDD). The acquisition of EEG experimental data involved 32 MDD patients and 29 healthy controls. A feature matrix is constructed involving frequency decomposition of EEG data based on power spectrum density (PSD) using the Welch method. Those PSD features were selected, which were statistically significant. To improve interpretability, the best features are first selected from feature space via the non-dominated sorting genetic (NSGA-II) evolutionary algorithm. The best features are utilized for support vector machine (SVM), and k-nearest neighbors ( k-NN) classifiers, and the results are then correlated with features to improve the interpretability. The results show that the features (gamma bands) extracted from the left temporal brain regions can distinguish MDD patients from control significantly. The proposed best solution by NSGA-II gives an average sensitivity of 93.3%, specificity of 93.4% and accuracy of 93.5%. The complete framework is published as open-source at https://github.com/ehw- fit/eeg- mdd.

Automated summary

In this paper, an interpretable EEG-based solution for the diagnostics of MDD is proposed, which involves the acquisition of EEG data from MDD patients and healthy controls. The best features are selected using the NSGA-II algorithm and utilized for SVM and k-NN classifiers. The results show that gamma bands extracted from the left temporal brain regions can significantly distinguish MDD patients from controls. The proposed solution achieves high sensitivity, specificity, and accuracy.