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A novel EEG-based major depressive disorder detection framework with two-stage feature selection
Yujie Li1, Yingshan Shen1, Xiaomao Fan2
1School of Computer Science, South China Normal University, Guangzhou, China.
BMC Medical Informatics and Decision Making
|August 6, 2022
Summary
This study introduces an advanced framework for detecting major depressive disorder (MDD) using electroencephalogram (EEG) signals. The novel approach achieves state-of-the-art accuracy in identifying MDD and assessing its severity.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Major Depressive Disorder (MDD) significantly impacts daily life and work.
- Accurate and early detection of MDD is crucial for effective treatment.
Purpose of the Study:
- To develop a novel automatic framework for detecting Major Depressive Disorder (MDD) using electroencephalogram (EEG) signals.
- To enhance the accuracy and efficiency of MDD screening and diagnosis.
Main Methods:
- Feature extraction from EEG signals within specific frequency bands.
- A two-stage feature selection method (PAR) combining Pearson correlation coefficient (PCC) and recursive feature elimination (RFE).
- Application of machine learning models including Support Vector Machine (SVM), Logistic Regression (LR), and Linear Regression (LNR) for MDD detection.
Main Results:
- The proposed framework achieved high accuracy (0.9895) and F1-score (0.9846) for MDD detection.
- A high regression determination coefficient (R²) of 0.9479 was obtained for MDD severity assessment.
- Outperformed existing MDD detection methods in terms of accuracy and F1-score.
Conclusions:
- The developed MDD detection framework demonstrates state-of-the-art performance.
- Potential for deployment in medical systems to assist physicians in screening MDD patients.
- Offers a promising tool for objective and efficient MDD diagnosis.
