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

References

  1. Acharya UR, Oh SL, Hagiwara Y et al (2018) Automated EEG-based screen ing of depression using deep convolutional neural network[J]. Comput Methods Programs Biomed 161:103–113

    Article  Google Scholar 

  2. Acharya UR, Oh SL, Hagiwara Y et al (2018) Automated EEG-based screening of depression using deep convolutional neural network[J]. Comput Methods Prog Biomed 161:103–113

    Article  Google Scholar 

  3. Ali A, Zhu Y, Chen Q et al (2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks[C]. In: 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS). IEEE, Piscataway, pp 125–132

  4. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing[J]. Multimedia Tools Appl 80(20):31401–31433

    Article  Google Scholar 

  5. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks[J]. Inf Sci 577:852–870

    Article  MathSciNet  Google Scholar 

  6. Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction[J]. Neural Netw 145:233–247

    Article  Google Scholar 

  7. Anderson PS (1974) An oblique, poly-cylindrical, orthographic azimuthal equidistant cartographic projection: its purpose, construction and theory[J]. Cartography 8(4):182–186

    Article  Google Scholar 

  8. Ay B, Yildirim O, Talo M et al (2019) Automated depression detection using deep representation and sequence learning with EEG signals[J]. J Med Syst 43(7):1–12

    Article  Google Scholar 

  9. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult[J]. IEEE Trans Neural Netw 5(2):157–166

    Article  Google Scholar 

  10. Bhatti UA, Huang M, Wu D et al (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems[J]. Enterp Inf Syst 13(3):329–351

    Article  Google Scholar 

  11. Cai H, Han J, Chen Y et al (2018) A pervasive approach to EEG-based depression detection[J]. Complexity 2018:1–13

    Google Scholar 

  12. Duan L, Duan H, Qiao Y et al (2020) Machine learning approaches for MDD detection and emotion decoding using EEG signals[J]. Front Human Neurosci 14:284

    Article  Google Scholar 

  13. Graves A (2012) Long short-term memory[J]

  14. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  15. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  16. Hosseinifard B, Moradi MH, Rostami R (2013) Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal[J]. Comput Methods Programs Biomed 109(3):339–345

    Article  Google Scholar 

  17. Hu B, Majoe D, Ratcliffe M et al (2011) EEG-based cognitive interfaces for ubiquitous applications: developments and challenges [J]. IEEE Intell Syst 26(5):46–53

    Article  Google Scholar 

  18. Kang M, Kwon H, Park JH et al (2020) Deep-asymmetry: Asymmetry matrix image for deep learning method in pre-screening depression[J]. Sensors 20 (22):6526

    Article  Google Scholar 

  19. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  20. Li X, La R, Wang Y et al (2019) EEG-based mild depression recognition using convolutional neural network[J]. Med Biol Eng Comput 57(6):1341–1352

    Article  Google Scholar 

  21. Li J, Struzik Z, Zhang L et al (2015) Feature learning from incomplete EEG with denoising autoencoder [J]. Neurocomputing 165:23–31

    Article  Google Scholar 

  22. Mahato S, Paul S (2019) Detection of major depressive disorder using linear and non-linear features from EEG signals[J]. Microsyst Technol 25(3):1065–1076

    Article  Google Scholar 

  23. Mumtaz W (2016) MDD Patients and Healthy Controls EEG Data (New). figshare

  24. Mumtaz W, Qayyum A (2019) A deep learning framework for automatic diagnosis of unipolar depression[J]. Int J Med Inf 132:103983

    Article  Google Scholar 

  25. Mumtaz W, Xia L, Ali SSA, Yasin MAM, Ali SSA, Malik AS (2017) A wavelet-based technique to predict treatment out-come for majordepressive disorder. In: PLOS ONE. Public Library of Science (PLOS), San Francisco, pp 1–30

  26. Mumtaz W (2016) MDD Patients and Healthy Controls EEG Data (New). figshare. Dataset. https://doi.org/10.6084/m9.figshare.4244171.v2

  27. Organization WH et al (2017) Depression and Other Common Mental Disorders: Global Health Estimates

  28. Rohani DA, Springer A, Hollis V, Bardram JE, Whittaker S (2020) Recommending activities for mental health and well-being: insights from two user studies. IEEE Trans Emerg Top Comput XX:1

    Google Scholar 

  29. Saeedi A, Saeedi M, Maghsoudi A et al (2021) Major depressive disorder diagnosis based on effective connectivity in EEG signals: A convolutional neural network and long short-term memory approach[J]. Cogn Neurodyn 15(2):239–252

    Article  Google Scholar 

  30. Sandheep P, Vineeth S, Poulose M et al (2019) Performance analysis of deep learning CNN in classification of depression EEG signals[C]. In: TENCON 2019-2019 IEEE Region 10 Conference (TENCON). IEEE, Piscataway, pp 1339–1344

  31. Seal A, Bajpai R, Agnihotri J et al (2021) DeprNet: A deep convolution neural network framework for detecting depression using EEG[J]. IEEE Trans Instrum Meas 70:1–13

    Article  Google Scholar 

  32. Sharma G, Parashar A, Joshi AM (2021) DepHNN: a novel hybrid neural network for electroencephalogram (EEG)based screening of depression[J]. Biomed Signal Process Control 66:102393

    Article  Google Scholar 

  33. Thoduparambil PP, Dominic A, Varghese SM (2020) EEG-based deep learning model for the automatic detection of clinical depression[J]. Phys Eng Sci Med 43(4):1349–1360

    Article  Google Scholar 

  34. Uyulan C, Ergüzel TT, Unubol H et al (2021) Major depressive disorder classification based on different convolutional neural network models: Deep learning approach[J]. Clin EEG Neurosci 52(1):38–51

    Article  Google Scholar 

  35. World Federation for Mental Health (2012) Depression: a global crisis. World Mental Health Day, October, 2012, 10: 2012

Download references

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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