A novel EEG-based major depressive disorder detection framework with two-stage feature selection

Abstract

Abstract Background Major depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people’s daily life and work seriously. Methods In this work, we present a novel automatic MDD detection framework based on EEG signals. First of all, we derive highly MDD-correlated features, calculating the ratio of extracted features from EEG signals at frequency bands between $$\beta$$ β and $$\alpha$$ α . Then, a two-stage feature selection method named PAR is presented with the sequential combination of Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), where the advantages lie in minimizing the feature searching space. Finally, we employ widely used machine learning methods of support vector machine (SVM), logistic regression (LR), and linear regression (LNR) for MDD detection with the merit of feature interpretability. Results Experiment results show that our proposed MDD detection framework achieves competitive results. The accuracy and $$F_{1}$$ F 1 score are up to 0.9895 and 0.9846, respectively. Meanwhile, the regression determination coefficient $$R^2$$ R 2 for MDD severity assessment is up to 0.9479. Compared with existing MDD detection methods with the best accuracy of 0.9840 and $$F_1$$ F 1 score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance. Conclusions Development of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients.

Keywords

• Depression detection
• Two-stage feature selection
• EEG