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With the increase of social pressure, depression has become a common psychological disorder. Traditional self-assessment scales for diagnosis are highly subjective, and electroencephalography (EEG) is a commonly used objective assessment tool for depression. To increase the precision of the widely used algorithms for the diagnosis of depression, this paper uses a combination of one-dimensional convolutional neural networks (1D-CNN) and gated recurrent units (GRUs) to extract local and temporal features of the EEG signal. Experiments show that the 1D-CNN-GRU model has better performance compared with the single network algorithm, the accuracy in the public dataset is 98%. In comparison to 1D-CNN and GRU models, which is 4% and 3% higher, respectively. In order to obtain a more accurate index of depression level, this paper uses different method to integrate the subjective PHQ-9 self-assessment scale results with the objective depression level scores obtained from 1D-CNN-GRU. The final score is fine-tuned on the basis of integrated score. This work can achieve more accurate detection of depression and assist doctors in diagnosis.

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A Novel Method for Calculating Depression Level Based on Hybrid Neural Networks and Subjective Scales

  • 会议论文

  • 发表日期：

    2025年

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  • Zhuozheng Wang,
  • Keyuan Li,
  • Xixi Zhao

摘要

With the increase of social pressure, depression has become a common psychological disorder. Traditional self-assessment scales for diagnosis are highly subjective, and electroencephalography (EEG) is a commonly used objective assessment tool for depression. To increase the precision of the widely used algorithms for the diagnosis of depression, this paper uses a combination of one-dimensional convolutional neural networks (1D-CNN) and gated recurrent units (GRUs) to extract local and temporal features of the EEG signal. Experiments show that the 1D-CNN-GRU model has better performance compared with the single network algorithm, the accuracy in the public dataset is 98%. In comparison to 1D-CNN and GRU models, which is 4% and 3% higher, respectively. In order to obtain a more accurate index of depression level, this paper uses different method to integrate the subjective PHQ-9 self-assessment scale results with the objective depression level scores obtained from 1D-CNN-GRU. The final score is fine-tuned on the basis of integrated score. This work can achieve more accurate detection of depression and assist doctors in diagnosis.