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Joint International Conference on Pervasive Computing and the Networked World

ICPCA/SWS 2012: Pervasive Computing and the Networked World pp 848–861Cite as

An EEG Based Pervasive Depression Detection for Females

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 7719))

Abstract

Recently, depression detection is mainly completed by some rating scales. This procedure requires attendance of physicians and the results may be more subjective. To meet emergent needs of objective and pervasive depression detection, we propose an EEG based approach for females. In the experiment, EEG of 13 depressed females and 12 age matched controls were collected in a resting state with eyes closed. Linear and nonlinear features extracted from artifact-free EEG epochs were subjected to statistical analysis to examine the significance of differences. Results showed that differences were significant for some EEG features between two groups (p<0.05) and the classification rates reached up to 92.9% and 94.2% with KNN and BPNN respectively. Our methods suggest that the discrimination of depressed females from controls is possible. We expect that our EEG based approach could be a pervasive assistant diagnosis tool for psychiatrists and health care specialists.

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Author information

Authors and Affiliations

  1. The School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Xiaowei Zhang, Bin Hu, Lin Zhou & Jing Chen

  2. The School of Computing, Telecommunications and Networks, Birmingham City University, Birmingham, B42 2SU, UK

    Philip Moore

Authors
  1. Xiaowei Zhang
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  2. Bin Hu
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  3. Lin Zhou
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  4. Philip Moore
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  5. Jing Chen
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Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Heping Road 1178, Wuchang District, 430081, Wuhan, Hubei, China

    Qiaohong Zu

  2. Hayes Park Central, Fujitsu Laboratories of Europe Ltd., Hayes End Road, UB4 8FE, Hayes, Middlesex, UK

    Bo Hu

  3. Department of Electrical and Electronics Engineering, Aksaray University, Merkez Kampüsü, 68100, Aksaray, Turkey

    Atilla Elçi

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhang, X., Hu, B., Zhou, L., Moore, P., Chen, J. (2013). An EEG Based Pervasive Depression Detection for Females. In: Zu, Q., Hu, B., Elçi, A. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37015-1_74

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  • DOI: https://doi.org/10.1007/978-3-642-37015-1_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37014-4

  • Online ISBN: 978-3-642-37015-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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