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A Graph Theory Analysis on Distinguishing EEG-Based Brain Death and Coma

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

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Abstract

Electroencephalogram (EEG) is always used to diagnosis the patients consciousness clinically because it is safe and easy to be record from patients. The aim of this paper is to analysis the relations between each channel in order to find out the brain network of brain death and coma patients particularity. In this paper, we use 10 adult patients’ EEG data to calculate the partial directed coherence (PDC) and build the average brain network for the two groups’ data after t-test based on the PDC results. Results showed that, these two clinical data are at most difference in the network parameters of degree, centrality and cluster coefficient as the threshold of PDC is set of 0.3. The time-varying connectivity could lead to better understanding of non-symmetric relations between different EEG channels and application in prediction of patients in brain death or coma state.

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Acknowledgements

This work was supported in part by KAKENHI (15K15955 and 15H04002).

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Authors and Affiliations

  1. Saitama Institute of Technology, Fusaiji 1690, Fukaya, Saitama, 3690293, Japan

    Gaochao Cui & Jianting Cao

  2. Brain Science Institute, RIKEN, Hirosawa 2-1, Wakoshi, Saitama, 3510198, Japan

    Li Zhu & Andrzej Cichocki

  3. Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 1030027, Japan

    Qibin Zhao

  4. Department of Cognitive Science, Xiamen University, No. 422, Siming South Road, Xiamen, 361005, Fujian, China

    Li Zhu

Authors
  1. Gaochao Cui
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  2. Li Zhu
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  3. Qibin Zhao
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  4. Jianting Cao
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  5. Andrzej Cichocki
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Corresponding author

Correspondence to Gaochao Cui .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Cui, G., Zhu, L., Zhao, Q., Cao, J., Cichocki, A. (2017). A Graph Theory Analysis on Distinguishing EEG-Based Brain Death and Coma. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_62

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  • DOI: https://doi.org/10.1007/978-3-319-70093-9_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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