An Enhanced Approach in Predicting and Classifying Major Depressive Disorder Using Bi-GRU with Attention Mechanism and Transfer Learning
Received: 16 May 2025 | Revised: 10 July 2025 | Accepted: 19 July 2025 | Online: 6 October 2025
Corresponding author: Udutala Mahender
Abstract
Major Depressive Disorder (MDD) is a serious issue in medical research due to its high prevalence and significant impact on the quality of life of individuals, leading to disability, comorbidity, and an increased risk of suicide. Accurate and early diagnosis is crucial for effective treatment, yet predicting MDD remains challenging due to its complex etiology, overlapping symptoms with other psychiatric disorders, and the subjective nature of traditional diagnostic methods. This study proposes RAG-EEGNet, a novel approach to detect MDD using EEG data, integrating advanced feature extraction and selection techniques with deep learning models. Initially, Boruta was used for comprehensive feature extraction, followed by Cuckoo Search Optimization (CSO) to select impactful features. A hybrid model is employed for classification, combining ResNet-50 and a Bi-GRU enhanced by an attention mechanism. The results show a significant improvement in the detection of MDD with an accuracy of 99.01%, precision of 100%, recall of 99.24%, F1-score of 99.12%, and ROC-AUC of 99.0%, demonstrating the efficacy of the proposed approach, highlighting the critical role of EEG data in diagnosing and predicting mental diseases.
Keywords:
Major Depressive Disorder(MDD), Boruta feature extraction, Cuckoo Search Optimization (CSO), ResNet-50, Bi-GRU, attention mechanismDownloads
References
Depressive disorder (depression)," World Health Organization. https://www.who.int/news-room/fact-sheets/detail/depression.
L. Cui et al., "Major depressive disorder: hypothesis, mechanism, prevention and treatment," Signal Transduction and Targeted Therapy, vol. 9, no. 1, Feb. 2024, Art. no. 30.
J. D. Smith et al., "Collaborative care for depression management in primary care: A randomized roll-out trial using a type 2 hybrid effectiveness-implementation design," Contemporary Clinical Trials Communications, vol. 23, Sep. 2021, Art. no. 100823.
D. Watts, R. F. Pulice, J. Reilly, A. R. Brunoni, F. Kapczinski, and I. C. Passos, "Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis," Translational Psychiatry, vol. 12, no. 1, Aug. 2022, Art. no. 332.
"Major Depression - National Institute of Mental Health (NIMH)," National Institure of Mental Health. https://www.nimh.nih.gov/health/statistics/major-depression.
E. Zhang, Z. Huang, Z. Zang, X. Qiao, J. Yan, and X. Shao, "Identifying circulating biomarkers for major depressive disorder," Frontiers in Psychiatry, vol. 14, Aug. 2023.
H. Cai, Z. Qu, Z. Li, Y. Zhang, X. Hu, and B. Hu, "Feature-level fusion approaches based on multimodal EEG data for depression recognition," Information Fusion, vol. 59, pp. 127–138, Jul. 2020.
R. Schaakxs, H. C. Comijs, R. C. van der Mast, R. A. Schoevers, A. T. F. Beekman, and B. W. J. H. Penninx, "Risk Factors for Depression: Differential Across Age?," The American Journal of Geriatric Psychiatry, vol. 25, no. 9, pp. 966–977, Sep. 2017.
A. U. Patil et al., "Review of EEG-based neurofeedback as a therapeutic intervention to treat depression," Psychiatry Research: Neuroimaging, vol. 329, Mar. 2023, Art. no. 111591.
D. F. Santomauro et al., "Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic," The Lancet, vol. 398, no. 10312, pp. 1700–1712, Nov. 2021.
R. H. Salk, J. S. Hyde, and L. Y. Abramson, "Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms," Psychological Bulletin, vol. 143, no. 8, pp. 783–822, 2017.
W. J. Yan, Q. N. Ruan, and K. Jiang, "Challenges for Artificial Intelligence in Recognizing Mental Disorders," Diagnostics, vol. 13, no. 1, Jan. 2023, Art. no. 2.
B. Pandurangaraju, B. Vijayalakshmi, S. A. Kumar, K. U. K. Reddy, S. Karimullah, and F. Shaik, "Optimizing Natural Language Understanding: A Comprehensive Study of Python’s Top Libraries," in 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany, Oct. 2024, pp. 559–563.
H. Wang et al., "Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study," Journal of Affective Disorders, vol. 321, pp. 8–15, Jan. 2023.
Y. Li, L. Qian, G. Li, and Z. Zhang, "Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study," Frontiers in Neuroscience, vol. 17, Jun. 2023.
Z. Shen et al., "Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework," Sensors, vol. 22, no. 14, Jan. 2022, Art. no. 5420.
A. Khosla, P. Khandnor, and T. Chand, "Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis," Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 108–142, Jan. 2022.
S. F. Begum et al., "Emotion Recognition from Physiological Signals Using Ensembled Machine Learning Strategy," in 2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC), Bengaluru, India, May 2024, pp. 1–8.
C. T. Lin et al., "Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment," IEEE Access, vol. 5, pp. 10612–10621, 2017.
L. Schmaal et al., "Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group," Molecular Psychiatry, vol. 22, no. 6, pp. 900–909, Jun. 2017.
G. Okada et al., "Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data," Journal of Affective Disorders, vol. 326, pp. 262–266, Apr. 2023.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778.
F. Zeng, R. Tang, and Y. Wang, "User Personalized Recommendation Algorithm Based on GRU Network Model in Social Networks," Mobile Information Systems, vol. 2022, no. 1, 2022, Art. no. 1487586.
M. B. Kursa, A. Jankowski, and W. R. Rudnicki, "Boruta – A System for Feature Selection," Fundamenta Informaticae, vol. 101, no. 4, pp. 271–285, Jul. 2010.
A. H. Gandomi, X. S. Yang, and A. H. Alavi, "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems," Engineering with Computers, vol. 29, no. 1, pp. 17–35, Jan. 2013.
R. Wang, Y. Hao, Q. Yu, M. Chen, I. Humar, and G. Fortino, "Depression Analysis and Recognition Based on Functional Near-Infrared Spectroscopy," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 12, pp. 4289–4299, Sep. 2021.
Y. Dong and X. Yang, "A hierarchical depression detection model based on vocal and emotional cues," Neurocomputing, vol. 441, pp. 279–290, Jun. 2021.
B. Ay et al., "Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals," Journal of Medical Systems, vol. 43, no. 7, May 2019, Art. no. 205.
W. Zaremba, I. Sutskever, and O. Vinyals, "Recurrent Neural Network Regularization." arXiv, Feb. 19, 2015.
S. Zhang et al., "The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook," Brain Sciences, vol. 13, no. 10, Oct. 2023, Art. no. 1462.
S. Y. Kim, "Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs," Bioengineering, vol. 10, no. 6, Jun. 2023, Art. no. 701.
E. N. Pitsik et al., "The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder," Chaos, Solitons & Fractals, vol. 167, Feb. 2023, Art. no. 113041.
M. K. Myee, R. D. C. Rebekah, T. Deepa, G. D. Zion, and K. Lokesh, "Detection of Depression in Social Media Posts using Emotional Intensity Analysis," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16207–16211, Oct. 2024.
J. Wang et al., "Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework," Brain Sciences, vol. 13, no. 11, Nov. 2023, Art. no. 1590.
S. Venkatapathy et al., "Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity," Frontiers in Psychiatry, vol. 14, Mar. 2023.
T. Zhao and G. Zhang, "Detecting Major Depressive Disorder by Graph Neural Network Exploiting Resting-State Functional MRI," in Neural Information Processing, 2023, pp. 255–266.
M. Xia, Y. Zhang, Y. Wu, and X. Wang, "An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification," IEEE Access, vol. 11, pp. 41337–41347, 2023.
Z. Xia, Y. Fan, K. Li, Y. Wang, L. Huang, and F. Zhou, "DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI," Electronics, vol. 12, no. 24, Dec. 2023, Art. no. 5040.
B. H. Bhavani, M. Sreenatha, and N. C. Kundur, "Diagnosis and Classification of Depressive Disorders using ML and DL Models," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21383–21389, Apr. 2025.
Y. Xu et al., "Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning," Sensors, vol. 23, no. 20, Jan. 2023, Art. no. 8639.
M. Z. Uddin, K. K. Dysthe, A. Følstad, and P. B. Brandtzaeg, "Deep learning for prediction of depressive symptoms in a large textual dataset," Neural Computing and Applications, vol. 34, no. 1, pp. 721–744, Jan. 2022.
A. Gulli and S. Pal, Deep Learning with Keras. Packt Publishing Ltd, 2017.
"EEG Psychiatric Disorders Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/shashwatwork/eeg-psychiatric-disorders-dataset.
D. M. W. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." arXiv, Oct. 11, 2020.
B. Liu, H. Chang, K. Peng, and X. Wang, "An End-to-End Depression Recognition Method Based on EEGNet," Frontiers in Psychiatry, vol. 13, Mar. 2022.
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