Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

Journal: Journal of affective disorders
Published Date: Jun 10, 2025

Abstract

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learning models with electroencephalography (EEG) data and clinical features. Utilizing Python for data preprocessing and feature extraction, we analyzed 370 patients diagnosed with either UPD or BPD. The experimental design featured 5-fold cross-validation and leave-one-out cross-validation to assess model generalization and optimize hyperparameters through grid search. Models included Support Vector Machine, Random Forest, and neural networks such as Fully Connected Neural Network(FCNN), Recurrent Neural Network, Long Short-Term Memory networks, and Transformers. Evaluation metrics highlighted FCNN's superior performance with 76 % accuracy, 80 % sensitivity, 73 % specificity, and 76 % F1-score. Results underscore the importance of EEG biomarkers, particularly beta band activity, in distinguishing between the two conditions. The study demonstrates the potential of deep learning in identifying complex mental disorder patterns and advocates for a shift towards data-driven diagnostics in mood disorders. Future research should aim to enhance model interpretability, integrate multimodal data, and develop advanced feature extraction techniques to further precision psychiatry.

Authors

  • Xinyu Liu
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Bingxu Chen
  • Haoran Zhang
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yi Cui
    Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Yingtan Wang
    Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
  • Tong Zhao
  • Yuxiang Yan
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
  • Sha Sha
    The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
  • Yanping Ren
    Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
  • Ling Zhang
  • Xixi Zhao
    Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China. Electronic address: zhaoxixi@ccmu.edu.cn.
  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.

Keywords

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