DBN: A Dual-Branch Network for Detecting Multiple Categories of Mental Disorders

• Published in: Information (Basel) Vol. 16; no. 9; p. 755
• Main Authors: Zhang, Longhao, Cui, Hongzhen, Peng, Yunfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Ablation; Accuracy; Algorithms; Anxiety; Attention deficit hyperactivity disorder; Brain research; Categories; Complexity; Deep learning; Diagnosis; Dual-Branch feature extraction strategy; dynamic routing; Effectiveness; Efficiency; Electroencephalography; Feature extraction; Logistics; Machine learning; Medical research; Medicine, Experimental; Mental depression; Mental disorders; Mental illness; Neural networks; Physiology; Post traumatic stress disorder; QEEG features; Risk factors; SHAP analysis; Stigma; Transmission efficiency; Maryland; Taiwan
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info16090755

Mental disorders (MDs) constitute significant risk factors for self-harm and suicide. The incidence of MDs has been increasing annually, primarily due to inadequate diagnosis and intervention. Early identification and timely intervention can effectively slow the progression of MDs and enhance the quality of life. However, the high cost and complexity of in-hospital screening exacerbate the psychological burden on patients. Moreover, existing studies primarily focus on the identification of individual subcategories and lack attention to model explainability. These approaches fail to adequately address the complexity of clinical demands. Early screening of MDs using EEG signals and deep learning techniques has demonstrated simplicity and effectiveness. To this end, we constructed a Dual-Branch Network (DBN) leveraging resting-state Quantitative Electroencephalogram (QEEG) features. The DBN is designed to enable the detection of multiple categories of MDs. Firstly, a dual-branch feature extraction strategy was designed to capture multi-dimensional latent features. Further, we propose a Multi-Head Attention Mechanism (MHAM) that integrates dynamic routing. This architecture assigns greater weights to key elements and enhances information transmission efficiency. Finally, the diagnosis is derived from a fully connected layer. In addition, we incorporate SHAP analysis to facilitate feature attribution. This technique elucidates the contribution of significant features to MD detection and improves the transparency of model predictions. Experimental results demonstrate the effectiveness of DBN in detecting various MD categories. The performance of DBN surpasses that of traditional machine learning models. Ablation studies further validate the architectural soundness of DBN. The DBN effectively reduces screening complexity and demonstrates significant potential for clinical applications.