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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 245–257Cite as

Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12534)

Abstract

Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows that the proposed hybrid model achieve highest accuracy, f1 score 99.66%, 99.93% and 98.87%, 99.12% for eye open and eye close dataset respectively in comparison to state of the art methods. Based on high performance, the proposed hybrid approach can be used for assessment of depression for clinical applications and can deployed remotely in hospital or private clinics for clinical evaluation.

Keywords

  • EEG
  • Depression
  • Anxiety
  • Electroencephalographic
  • Mental disorder

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Notes

  1. 1.

    Code: https://github.com/RespectKnowledge.

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Author information

Authors and Affiliations

  1. University of Burgundy, Dijon, France

    Abdul Qayyum

  2. School of Information Technology, Deakin University, Geelong, Australia

    Imran Razzak

  3. National University of Sciences and Technology, Islamabad, Pakistan

    Wajid Mumtaz

Authors
  1. Abdul Qayyum
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  2. Imran Razzak
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  3. Wajid Mumtaz
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Corresponding author

Correspondence to Imran Razzak .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Dr. Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Prof. James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Prof. Irwin King

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Qayyum, A., Razzak, I., Mumtaz, W. (2020). Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-63836-8_21

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  • Publisher Name: Springer, Cham

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