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Real-Time Human Depression Diagnosis System Using Brain Wave Analysis

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Advanced Multimedia and Ubiquitous Engineering (FutureTech 2017, MUE 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 448))

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Abstract

This study has the goal of developing a diagnosis system to detect human depression in real time to assist in the diagnosis of a doctor. The developed system may grasp the concentration and depression level of a patient using brainwave data acquired in real time. The depression detection index used in the system is the frontal brain asymmetry (FBA), which is based on the asymmetric phenomenon of depressed patients. In this study, an experiment was conducted with 40 depressed/normal subjects in order to verify the reliability of the developed system. The results proved that the system diagnosed the depression level in real time. It can be used to develop therapy programs for various nervous and mental disorders.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01059253).

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Authors and Affiliations

  1. Department of Computer Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul, 143-747, Korea

    Dongmin Shin, Yunjin Nam, Dongil Shin & Dongkyoo Shin

Authors
  1. Dongmin Shin
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  2. Yunjin Nam
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  3. Dongil Shin
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  4. Dongkyoo Shin
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Corresponding author

Correspondence to Dongkyoo Shin .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  3. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, Adelaide, Australia

    Kim-Kwang Raymond Choo

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Shin, D., Nam, Y., Shin, D., Shin, D. (2017). Real-Time Human Depression Diagnosis System Using Brain Wave Analysis. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_67

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  • DOI: https://doi.org/10.1007/978-981-10-5041-1_67

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5040-4

  • Online ISBN: 978-981-10-5041-1

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