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Detection of Depression from Brain Signals: A Review Study

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Health Information Science (HIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11148))

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Abstract

Depression is a very common brain disorder now these days. It normally affects 10-15% of the population in the world. The untreated depression may lead to various undesirable consequences such as suicide, poor physical health, self-harm, etc. There is no age group left behind from this disorder. Depression affects negatively on an individual’s personal, professional and social life. Detection of depression from brain signals (Such as Electroencephalogram (EEG)) is a challenging task for both research and neurologist due to the non-stationary and chaotic nature of EEG signals. The depression detection at an early stage is very important because it can help patients to obtain the best treatment on time and we can prevent them from harmful consequences. Aim of this study is to provide the current scenery of detection of depression from EEG signals. The EEG signals use as a tool to read the brain activity of an individual. The results of EEG test help us to perform the different techniques to detect the depression. In addition, this paper provides the general idea of different stages of depression detection such as the data collection, pre-processing, feature extraction- selection and classification, it also reports the existing techniques in this area. End of this work, finally we can find the limitations of existing work and the directions for future work.

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Authors and Affiliations

  1. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Prabhjyot Kaur, Siuly Siuly & Yuan Miao

Authors
  1. Prabhjyot Kaur
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  2. Siuly Siuly
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  3. Yuan Miao
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Corresponding author

Correspondence to Siuly Siuly .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. James Cook University, Cairns, QLD, Australia

    Ickjai Lee

  3. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Footscray, VIC, Australia

    Hua Wang

  6. James Cook University, Cairns, QLD, Australia

    Wei Xiang

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Kaur, P., Siuly, S., Miao, Y. (2018). Detection of Depression from Brain Signals: A Review Study. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-01078-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01077-5

  • Online ISBN: 978-3-030-01078-2

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