Skip to main content
SpringerLink
Log in

Multiscale Entropy Analysis of EEG Based on Non-uniform Time

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

Included in the following conference series:

  • 2419 Accesses

Abstract

To address the problem of loss of time series information when taking the mean value of the average signal from EEG data, a variance index is used to reconstruct the time series, and a multi-scale entropy method based on a non-uniform time window is proposed. The effectiveness of the method is verified in two data sets. The results show that the use of the variance of multi-scale time series can extract more effective features compared with average indicators. Compared with coarse-grained methods, the fine-grained method has better accuracy, but its time complexity is also higher. The uniform time window method has not only better accuracy, but also reduced time complexity.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zou, X., Lei, M.: Model recognition of surface EMG signals based on multi-scale maximum Lyapunov exponents. Chin. J. Biomed. Eng. 31(1), 7–12 (2012)

    Google Scholar 

  2. Zhao, L., Liang, Z., Wu, W., et al.: Analysis of EEG-related dimension changes in patients with epilepsy after biofeedback training. Chin. J. Biomed. Eng. 29(1), 71–76 (2010)

    Google Scholar 

  3. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)

    Article  Google Scholar 

  4. Richman, J.S.: Sample entropy statistics and testing for order in complex physiological signals. Commun. Stat. - Theory Methods 36(5), 1005–1019 (2007)

    Article  MathSciNet  Google Scholar 

  5. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 89(6), 068102 (2002)

    Article  Google Scholar 

  6. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71(2), 021906 (2005)

    Article  MathSciNet  Google Scholar 

  7. Heisz, J.J., Shedden, J.M., McIntosh, A.R.: Relating brain signal variability to knowledge representation. Neuroimage 63(3), 1384–1392 (2012)

    Article  Google Scholar 

  8. da Silva, F.L.: EEG and MEG: relevance to neuroscience. Neuron 80(5), 1112–1128 (2013)

    Article  Google Scholar 

  9. Wu, S.D., Wu, C.W., Lee, K.Y., et al.: Modified multiscale entropy for short-term time series analysis. Physica A 392(23), 5865–5873 (2013)

    Article  Google Scholar 

  10. Amoud, H., Snoussi, H., Hewson, D., et al.: Intrinsic mode entropy for nonlinear discriminant analysis. IEEE Signal Process. Lett. 14(5), 297–300 (2007)

    Article  Google Scholar 

  11. Labate, D., Foresta, F.L., Inuso, G., et al.: Multiscale entropy analysis of artifactual EEG recordings. Front. Artif. Intell. Appl. 234, 170–177 (2011)

    Google Scholar 

  12. Hu, M., Liang, H.: Adaptive multiscale entropy analysis of multivariate neural data. IEEE Trans. Biomed. Eng. 59(1), 12–15 (2012)

    Article  Google Scholar 

  13. Hu, M., Liang, H.: Variance entropy: a method for characterizing perceptual awareness of visual stimulus. Appl. Comput. Intell. Soft Comput. 2012, 1 (2012)

    Article  Google Scholar 

  14. Costa, M.D., Goldberger, A.L.: Generalized multiscale entropy analysis: application to quantifying the complex volatility of human heartbeat time series. Entropy 17(3), 1197–1203 (2015)

    Article  Google Scholar 

  15. Yin, Y., Shang, P., Feng, G.: Modified multiscale cross-sample entropy for complex time series. Appl. Math. Comput. 289, 98–110 (2016)

    MathSciNet  MATH  Google Scholar 

  16. Wu, Y., Shang, P., Li, Y.: Multiscale sample entropy and cross-sample entropy based on symbolic representation and similarity of stock markets. Commun. Nonlinear Sci. Numer. Simul. 56, 49–61 (2018)

    Article  MathSciNet  Google Scholar 

  17. Lehmann, D., Ozaki, H., Pal, I.: EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr. Clin. Neurophysiol. 67(3), 271–288 (1987)

    Article  Google Scholar 

  18. Liu, Z., Huang, J., Feng, X.: Construction of multi-scale depth convolution neural network behavior recognition model. Editorial Office Opt. Precis. Eng. 25(3), 799–805 (2017)

    Article  Google Scholar 

  19. Yang, Q., Li, S., Zhang, Y., et al.: Time-frequency analysis algorithm for multi-particle motion segmentation. J. Comput.-Aided Des. Comput. Graph. 29(12) (2017)

    Google Scholar 

  20. Pincus, S.M.: Approximate entropy (ApEn) as a complexity measure. Chaos 5, 110–117 (1995)

    Article  Google Scholar 

  21. Kosko, B.: Fuzzy entropy and conditioning. Inf. Sci. 40(2), 165–174 (1986)

    Article  MathSciNet  Google Scholar 

  22. Wang, J., Ning, X., Li, J., et al.: ECG multi-scale entropy analysis. In: Cutting-Edge Science Conference Papers (2004)

    Google Scholar 

  23. Ebrahimi, N., Maasoumi, E., Soofi, E.S.: Ordering univariate distributions by entropy and variance. J. Econometrics 90(2), 317–336 (1999)

    Article  MathSciNet  Google Scholar 

  24. Okazaki, R., Takahashi, T., Ueno, K., et al.: Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach. Front. Hum. Neurosci. 9, 106 (2015)

    Article  Google Scholar 

  25. Humeau-Heurtier, A., Mahé, G., Abraham, P.: Modified multiscale sample entropy computation of laser speckle contrast images and comparison with the original multiscale entropy algorithm. J. Biomed. Opt. 20(12), 121302 (2015)

    Article  Google Scholar 

  26. Diao, L., Wang, L., Lu, Y., et al.: Method of calculating text similarity threshold. J. Tsinghua Univ. (Sci. Technol.) 43(1), 108–111 (2003)

    Google Scholar 

  27. Bai, D., Qiu, T., Li, X.: Sample entropy and its application in EEG seizure detection. J. Biomed. Eng. 24(1), 200–205 (2007)

    Google Scholar 

  28. Iasemidis, L.D., Sackellares, J.C., Zaveri, H.P., Williams, W.J.: Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topogr. 2(3), 187–201 (1990)

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by research grants from the National Natural Science Foundation of China (61472270, 61672374, 61741212), Natural Science Foundation of Shanxi Province (201601D021073, 201801D121135), and Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2016139).

Author information

Authors and Affiliations

  1. Taiyuan University of Technology, Taiyuan, 030024, China

    Hongxia Deng, Jinxiu Guo, Xiaofeng Yang, Jinxiu Hou & Haifang Li

  2. North China Electric Power University, Beijing, 102206, China

    Haoqi Liu

Authors
  1. Hongxia Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinxiu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinxiu Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haoqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haifang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haifang Li .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

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

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

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

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deng, H., Guo, J., Yang, X., Hou, J., Liu, H., Li, H. (2019). Multiscale Entropy Analysis of EEG Based on Non-uniform Time. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31723-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation