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DCTNet: hybrid deep neural network-based EEG signal for detecting depression

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Abstract

Depression is a mood disorder that can affect people’s psychological problems. The current medical approach is to detect depression by manual analysis of EEG signals, however, manual analysis of EEG signals is cumbersome and time-consuming, requiring a lot of experience. Therefore, we propose a short time series base on convolutional neural network (CNN), called DCLNet, for depression classification. Firstly, the sample size and diversity of the dataset are enhanced by the clipping strategy. Then we superimposes different frequency domains and put them into a two-dimensional matrix according to the electrode position of the EEG, which was input to CNN to extract important features. Finally, the extracted features are put into the Long short-term memory network (LSTM) to capture the temporal information. The experimental results shows that the Accuracy of the model is 99.15%, Specificity is 99.01%, and Sensitivity is 99.30%. Compared with the current popular machine learning (ML) methods and deep learning (DL) models, DCTNet has excellent performance in the evaluation indexes of Accuracy, Specificity and Sensitivity.

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Data Availability

All data generated or analysed during this study are included in this published article.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China[61300098], the Natural Science Foundation of Heilongjiang Province[F201347], and the Fundamental Research Funds for the Central Universities[2572015DY07].

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    Authors and Affiliations

    1. Information and Computer Engineering Colege, Northeast Forestry University, 26 Hexing Road, Harbin, 150040, Heilongjiang Province, China

      Yu Chen & Sheng Wang

    2. School of Computer Science and Engineering, Guilin University of Aerospace Technology, No.2 Jinji Road, Guilin, Guangxi, China

      Jifeng Guo

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    1. Yu Chen
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    Correspondence to Jifeng Guo.

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    Sheng Wang and Jifeng Guo are contributed equally to this work.

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    Chen, Y., Wang, S. & Guo, J. DCTNet: hybrid deep neural network-based EEG signal for detecting depression. Multimed Tools Appl 82, 41307–41321 (2023). https://doi.org/10.1007/s11042-023-14799-y

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