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

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.

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

“…The data was recorded in a resting state (participants’ eyes were closed) for 90 s in a room with no noise [ 48 ]. A pervasive EEG collection device with three electrodes is used for data collection from the prefrontal lobe of participants because this part of the human brain relates to emotions and psychiatric conditions [ 49 ].…”

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

“…We used the K-nearest neighbors algorithm to assess the effectiveness of each of these feature selection methods. This proposal allowed the investigation of possible patterns resulting from the combination of techniques in multiple H&E datasets [61][62][63]. Due to the stochastic characteristic of the wrapper selection algorithms, the final performance of a combination was determined from the arithmetic average of 10 different runs of the selection step.…”

“…It is essential to observe that EL models can be developed with the most relevant features, exploring a single selection stage to reduce the search space and increase the accuracy of the system [13,15,18]. The use of feature selection through two stages can also be implemented [61][62][63]. In these cases, the strategies were applied to the ensemble of DL, and the results obtained were better than those obtained via a single stage.…”

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

“…Their method, utilizing EEG signals, achieved an accuracy of 80%. Separately, Li et al (2022) introduced an automatic depression detection framework built upon a two-stage feature selection method. This framework employed EEG signals, incorporating the Pearson correlation coefficient and recursive feature elimination techniques, achieving a remarkable accuracy of 98.95% when using SVM with derived features from the alpha and beta frequency bands.…”