ArticleBMC medical informatics and decision making2022
A novel EEG-based major depressive disorder detection framework with two-stage feature selection.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- DEP-TFDualNet: A Dual-Domain Attention Framework with Temporal-Frequency Fusion for Depression Recognition Using Three-Channel Frontal EEG.Sensors (Basel, Switzerland) · 2026Article
- Subject-Independent Depression Recognition from EEG Using an Improved Bidirectional LSTM with Dynamic Vector Routing.Bioengineering (Basel, Switzerland) · 2026Article
- Distinguishing Early Depression from Negative Emotion via Multi-Domain EEG Feature Fusion and Multi-Head Additive Attention Network.Entropy (Basel, Switzerland) · 2026Article
- MS-MDDNet: A Lightweight Deep Learning Framework for Interpretable EEG-Based Diagnosis of Major Depressive Disorder.Diagnostics (Basel, Switzerland) · 2026Article
- Mental health monitoring in 5G Edge-Enabled Cognitive IoT with Temporal Shift Transformer and integrated Stackelberg Game Theory and Nomadic People Optimizer.Scientific reports · 2025Article
- Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records.Scientific reports · 2025Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology.Frontiers in neuroscience · 2024Article
- Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder.BMC psychiatry · 2023Article
- Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
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7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people's daily life and work seriously.
methodsIn 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 [Formula: see text] and [Formula: see text]. 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.
resultsExperiment results show that our proposed MDD detection framework achieves competitive results. The accuracy and [Formula: see text] score are up to 0.9895 and 0.9846, respectively. Meanwhile, the regression determination coefficient [Formula: see text] for MDD severity assessment is up to 0.9479. Compared with existing MDD detection methods with the best accuracy of 0.9840 and [Formula: see text] score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance.
conclusionsDevelopment of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients.
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