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Real time acquisition and processing of massive electro-encephalographic signals for modeling by nonlinear statistics

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Abstract

Electro EncephaloGraphic (EEG) signals follow complex patterns, which are needed to analyse either for healthy or sick people. Instead of analyzing long rolls of EEG, to decide how a brain works given a task to solve, a index which can simplify massive data (EEG) information in a very simple plot in terms of an index: Average Bivariate MultiScale Entropy (ABMSE) This index is a modified version of a nonlinear statistic known as MultiScale Entropy (MSE) that resulted to be very useful for this task is exploited. Early results show the proposal statistic ABMSE concentrates massive complexity information in a single quantity that can be plotted per brain zone. Some ideas are discussed on the application of interactive engineering and design. In particular, the use of learning algorithms to distinguish different pathological scenarios based on EEG signal post processing. This will support neurologists, bioengineers and neuroscientists in healthy or sick people.

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References

  1. Shorvon, D.S.: The etiologic classification of epilepsy. Epilepsy 52(6), 1052–1057 (2011)

    Article  Google Scholar 

  2. Grant, A.C., Abdel-Baki, S.G., Weedon, J., et al.: EEG interpretation reliability and interpreter confidence: a large single-centre study. Epilepsy Behav. 32, 102–107 (2014)

  3. Rating, D.: Journal Club, Wie konstant ist die EEG-Befundung? Zeitschrift fr Epileptologie 27(2), 139–142 (2014)

  4. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  6. Eckmann, J.P., Ruelle, D.: Ergodic theory of chaos and strange attractors. Rev. Mod. Phys. 57, 617–656 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  7. Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Nat. Acad. Sci. USA 88(1), 2297–2301 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  8. Pincus, S.M.: Approximate entropy (ApEn) as a complexity measure. Chaos: an interdisciplinary. J. Nonlinear Sci. 1(5), 110–117 (1995)

    Google Scholar 

  9. Ben-Mizrachi, A., Procaccia, I., Grassberger, P.: Characterization of experimental (noisy) strange attractors. Phys. Rev. A 29(2), 975–977 (1984)

    Article  Google Scholar 

  10. Costa, M., Goldberger, A., Peng, C.K.: Multiscale entropy analysis of complex physiologic time series. Phys. Rev. 89(6), 068102(1)–068102(4) (2002)

    Google Scholar 

  11. Chon, K.H., Scully, C.G., Lu, S.: Approximate entropy for all signals. IEEE Eng. Med. Biol. Mag. 0739–5175, 18–23 (2009)

    Article  Google Scholar 

  12. Zavala-Yoé, R., Ramírez-Mendoza, R., Cordero, L.M.: Novel way to investigate evolution of children refractory epilepsy by complexity metrics in massive information. Springer Plus 4(437), 1–33 (2015)

    Google Scholar 

  13. Wu, S.D., Wu, C.W., Lin, S.G., Wang, C.C., Lee, K.Y.: Time series analysis using composite multiscale entropy. Entropy 15, 1069–1084 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  14. g.tec Medical Engineering Gmbh. Advanced Biosignal Acquisition Processing and Aanalysis, Products 2013/2014. www.gtec.at, Austria (2014)

  15. Gil-Nagel, A.: Manual de Electroencefalografia. McGraw-Hill Interamericana, Mexico (2001)

    Google Scholar 

  16. Doré, R., Pailhes, J., Fischer, X., Nadeau, J.P.: Identification of sensory variables towards the integration of user requirements into preliminary design. Int. J. Ind. Ergon. 37, 1–11 (2007)

    Article  Google Scholar 

  17. Fischer, X., Nadeau, J.P.: Interactive design: then and now. Res. Interact. Des. Springer Paris Paris. 3, 1–5 (2011)

    Google Scholar 

Download references

Acknowledgements

This research was supported by the Tecnológico de Monterrey. Funding was provided by Instituto Tecnológico y de Estudios Superiores de Monterrey (Grant No. #100).

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Authors and Affiliations

  1. School of Engineering and Science, Tecnológico de Monterrey, Calle del Puente, # 222, 4380, Mexico, DF, Mexico

    Ricardo Zavala-Yoé, Ricardo A. Ramírez-Mendoza & Ruben Morales-Menendez

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  1. Ricardo Zavala-Yoé
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  2. Ricardo A. Ramírez-Mendoza
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  3. Ruben Morales-Menendez
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Correspondence to Ricardo A. Ramírez-Mendoza.

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Zavala-Yoé, R., Ramírez-Mendoza, R.A. & Morales-Menendez, R. Real time acquisition and processing of massive electro-encephalographic signals for modeling by nonlinear statistics. Int J Interact Des Manuf 11, 427–433 (2017). https://doi.org/10.1007/s12008-016-0366-8

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  • DOI: https://doi.org/10.1007/s12008-016-0366-8

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