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Dynamic MEMD Associated with Approximate Entropy in Patients’ Consciousness Evaluation

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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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.

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References

  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. 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. Rehman, N., Mandic, D.: Multivariate empirical mode decomposition. Proc. R. Soc. A 466(2117), 1291–1302 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Tanaka, T., Mandic, D.: Complex empirical mode decomposition. IEEE Signal Process. Lett. 14(2), 101–104 (2006)

    Article  Google Scholar 

  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. Rehman, N., Mandic, D.: Empirical mode decomposition for trivariate signals. IEEE Trans. Signal Process. 58(3), 1059–1068 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  9. Pincus, S.M.: Approximate entropy (ApEn) as a measure of system complexity. Proc. Natl. Acad. Sci. 88, 110–117 (1991)

    Article  Google Scholar 

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Correspondence to Gaochao Cui .

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© 2016 Springer International Publishing AG

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Cui, G., Zhao, Q., Tanaka, T., Cao, J., Cichocki, A. (2016). Dynamic MEMD Associated with Approximate Entropy in Patients’ Consciousness Evaluation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

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