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With the increasing impact of affective disorders on global health, accurate identification of major depressive disorder (MDD) and bipolar disorder (BD) is crucial. The symptomatic similarity between these disorders complicates treatment decisions, affecting patient outcomes and long-term management. To overcome the limitations of traditional symptom-based diagnostics, this study integrates multi-source datasets, analyzing clinical and high-precision EEG data from 200 MDD and 200 BD patients. We extracted 360 metrics using feature engineering techniques and applied various machine learning algorithms, including random forest, support vector machine (SVM), and neural networks. Specifically, we addressed the temporal characteristics of Electroencephalogram (EEG) data by introducing Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and Transformer models. The Fully Connected Neural Network (FCNN) was selected as the optimal model due to its superior performance in accuracy, specificity, and sensitivity. We also emphasized the impact of model generalization on clinical applications, exploring variable importance and sub-band contributions in EEG. To prevent overfitting from large EEG data inputs, we proposed corresponding strategies and improvements. Our findings improve the classification accuracy of MDD and BD and highlight the value of EEG features in mental disorder identification, providing a foundation for developing efficient automated diagnostic tools and advancing precision medicine in affective disorder treatment.

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Research on the Application of Deep Neural Network in the Classification of EEG Data of Major Depressive Disorder and Bipolar Disorder and Optimization Strategy

  • 会议论文

  • 发表日期：

    2025年

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  • Zhuozheng Wang,
  • Bingxu Chen,
  • Xixi Zhao,
  • Xinyu Liu,
  • Xiaoyun Liu

摘要

With the increasing impact of affective disorders on global health, accurate identification of major depressive disorder (MDD) and bipolar disorder (BD) is crucial. The symptomatic similarity between these disorders complicates treatment decisions, affecting patient outcomes and long-term management. To overcome the limitations of traditional symptom-based diagnostics, this study integrates multi-source datasets, analyzing clinical and high-precision EEG data from 200 MDD and 200 BD patients. We extracted 360 metrics using feature engineering techniques and applied various machine learning algorithms, including random forest, support vector machine (SVM), and neural networks. Specifically, we addressed the temporal characteristics of Electroencephalogram (EEG) data by introducing Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and Transformer models. The Fully Connected Neural Network (FCNN) was selected as the optimal model due to its superior performance in accuracy, specificity, and sensitivity. We also emphasized the impact of model generalization on clinical applications, exploring variable importance and sub-band contributions in EEG. To prevent overfitting from large EEG data inputs, we proposed corresponding strategies and improvements. Our findings improve the classification accuracy of MDD and BD and highlight the value of EEG features in mental disorder identification, providing a foundation for developing efficient automated diagnostic tools and advancing precision medicine in affective disorder treatment.