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Dynamic MEMD Associated with Approximate Entropy in Patients’ Consciousness Evaluation

  • Gaochao CuiEmail author
  • Qibin Zhao
  • Toshihisa Tanaka
  • Jianting Cao
  • Andrzej Cichocki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9947)

Abstract

Electroencephalography (EEG) based preliminary examination has been widely used in diagnosis of brain diseases. Based on previous studies, clinical brain death determination also can be actualized by analyzing EEG signal of patients. Dynamic Multivariate empirical mode decomposition (D-MEMD) and approximate entropy (ApEn) are two kinds of methods to analyze brain activity status of the patients in different perspectives for brain death determination. In our previous studies, D-MEMD and ApEn methods were always used severally and it cannot analyzing the patients’ brain activity entirety. In this paper, we present a combine analysis method based on D-MEMD and ApEn methods to determine patients’ brain activity level. Moreover, We will analysis three different status EEG data of subjects in normal awake, comatose patients and brain death. The analyzed results illustrate the effectiveness and reliability of the proposed methods.

Keywords

Electroencephalography (EEG) Multivariate empirical mode decomposition (MEMD) Dynamic-MEMD Approximate entropy (ApEn) 

References

  1. 1.
    Yin, Y., Zhu, H., Tanaka, T., Cao, J.: Analyzing the EEG energy of healthy human, comatose patient and brain death using multivariate empirical mode decomposition algorithm. In: Proceedings of the 2012 IEEE International Conference on Signal Processing, vol. 1, pp. 148–151. IEEE Press (2012)Google Scholar
  2. 2.
    Yin, Y., Cao, J., Shi, Q., Mandic, D., Tanaka, T., Wang, R.: Analyzing the EEG energy of quasi brain death using MEMD. In: Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (CD-ROM) (2011)Google Scholar
  3. 3.
    Rehman, N., Mandic, D.: Multivariate empirical mode decomposition. Proc. R. Soc. A 466(2117), 1291–1302 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Tanaka, T., Mandic, D.: Complex empirical mode decomposition. IEEE Signal Process. Lett. 14(2), 101–104 (2006)CrossRefGoogle Scholar
  5. 5.
    Altaf, M., Gautama, T., Tanaka, T., Mandic, D.: Rotation invariant complex empirical mode decomposition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP 2007), Honolulu, HI, pp. 1009–1012 (2007)Google Scholar
  6. 6.
    Rehman, N., Mandic, D.: Empirical mode decomposition for trivariate signals. IEEE Trans. Signal Process. 58(3), 1059–1068 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Huang, N., Wu, M., Long, S., Shen, S., Qu, W., Gloersen, P., Fan, K.: A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc. R. Soc. Lond. A 459, 2317–2345 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C., Liu, H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454, 903–995 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Pincus, S.M.: Approximate entropy (ApEn) as a measure of system complexity. Proc. Natl. Acad. Sci. 88, 110–117 (1991)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Gaochao Cui
    • 1
    • 2
    Email author
  • Qibin Zhao
    • 1
    • 2
  • Toshihisa Tanaka
    • 3
  • Jianting Cao
    • 1
    • 2
  • Andrzej Cichocki
    • 2
  1. 1.Saitama Institute of TechnologyFukayaJapan
  2. 2.Brain Science Institute, RIKENWakoshiJapan
  3. 3.Tokyo University of Agriculture and TechnologyKoganei-shiJapan

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