Skip to main content
SpringerLink
Log in

Dynamic multivariate multiscale entropy based analysis on brain death diagnosis

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

The recently introduced multivariate multiscale sample entropy (MMSE) well evaluates the long correlations in multiple channels, so that it can reveal the complexity of multivariate biological signals. The existing MMSE algorithm deals with short time series statically whereas long time series are common for real-time computation in practical use. As a solution, we novelly proposed our dynamic MMSE (DMMSE) as an extension of MMSE. This helps us gain greater insight into the complexity of each section of time series, producing multifaceted and more robust estimates than the standard MMSE. The simulation results illustrated the feasibility and well performance in the brain death diagnosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Cao J T, Chen Z. ICA and complexity measures of EEG analysis in brain death determination. Advances in Cognitive Neurodynamics ICCN 2007. Berlin: Springer, 2008. 699–703

    Chapter  Google Scholar 

  2. Chen Z, Cao J T, Cao Y, et al. An empirical EEG analysis in brain death diagnosis for adults. Cogn Neurodynamics, 2008, 2: 257–271

    Article  Google Scholar 

  3. Cao J T. Analysis of the quasi-brain-death EEG data based on a robust ICA approach. Knowledge-Based Intelligent Information and Engineering Systems. Berlin: Springer, 2006. 1240–1247

    Chapter  Google Scholar 

  4. Shi Q W, Zhou W, Cao J T, et al. Brain-computer interface system using approximate entropy and EMD techniques. Advances in Swarm Intelligence. Berlin: Springer, 2010. 204–212

    Chapter  Google Scholar 

  5. Cao J T, Chen Z. Advanced EEG signal processing in brain death diagnosis. Signal Processing Techniques for Knowledge Extraction and Information Fusion. Berlin: Springer, 2008. 275–297

    Chapter  Google Scholar 

  6. Eelco F, Wijdicks M. Brain death worldwide. Neurology, 2002, 58: 20–25

    Article  Google Scholar 

  7. Taylor R M. Reexaming the definition and criteria of death. Semin Neurol, 1997, 17: 265–270

    Article  Google Scholar 

  8. Goshvarpour A, Goshvarpour A. Comparison of higher order spectra in heart rate signals during two techniques of meditation: Chi and kundalini meditation. Cogn Neurodynamic, 2013, 7: 39–46

    Article  Google Scholar 

  9. Fehr T. A hybrid model for the neural representation of complex mental processing in the human brain. Cogn Neurodynamic, 2013, 7: 89–103

    Article  Google Scholar 

  10. Pincus S M. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United State of America, 1991, 88: 2297–2301

    Article  MATH  MathSciNet  Google Scholar 

  11. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-heart C, 2000, 278: 2039–2049

    Google Scholar 

  12. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett, 2002, 89: 68–102

    Article  Google Scholar 

  13. Hu M, Liang H L. Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis. Cogn Neurodynamic, 2011, 5: 277–284

    Article  Google Scholar 

  14. Hu M, Liang H L. Adaptive multiscale entropy analysis of multivariate neural data. IEEE T Biomed Eng, 2011, 59: 12–15

    Google Scholar 

  15. Ahmed M U, Li L, Cao J T, et al. Multivariate multiscale entropy for brain consciousness analysis. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. Boston, 2011. 810–813

    Chapter  Google Scholar 

  16. Yang K, Cao J T, Wang R B, et al. Analyzing EEG of quasi-brain-death based on dynamic approximate entropy measures. Chinese J Bio Eng, 2011, 30: 27–33

    Google Scholar 

  17. Cao L, Mees A, Judd K. Dynamic from multivariate time series. Physica D, 1998, 121: 75–88

    Article  MATH  Google Scholar 

  18. Guatama T, Mandic D P, Van Hulle M M. The delay vector variance method for detecting determinism and nonlinearity in time series. Physica D, 2004, 190: 167–176

    Article  MathSciNet  Google Scholar 

  19. Li L, Saito Y, Looney D, et al. Data fusion via fission for the analysis of brain death. Evolving Intelligent Systems: Methodology and Applications. Berlin: Springer, 2010. 279–320

    Google Scholar 

  20. Ni L, Cao J, Wang R. Analyzing EEG of quasi-brain-death based on dynamic sample entropy measures. Comput Math Method M, 2013, 2013: 618743

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237, China

    Li Ni & RuBin Wang

  2. Department Electronic Engineering, Saitama Institute of Technology, Saitama, 369-0293, Japan

    JianTing Cao

  3. Brain Science Institute, RIKEN 2-1 Hirosawa, Saitama, 351-0198, Japan

    JianTing Cao

Authors
  1. Li Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JianTing Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. RuBin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to RuBin Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ni, L., Cao, J. & Wang, R. Dynamic multivariate multiscale entropy based analysis on brain death diagnosis. Sci. China Technol. Sci. 58, 425–433 (2015). https://doi.org/10.1007/s11431-014-5757-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-014-5757-0

Keywords

Search

Navigation