Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

  • Anbin Xiong
  • Xingang Zhao
  • Jianda Han
  • Guangjun Liu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 274)

Abstract

Surface electromyography (sEMG) has been widely applied to disease diagnosis, pathologic analysis and rehabilitation evaluation. It is the nonlinear summation of the electrical activity of the motor units in a muscle and can reflect the state of neuromuscular function. Traditional linear and statistical analysis methods have some significant limitations due to the short-term stationary and lower signal-noise ratio of sEMG. In this paper, we introduce chaotic analysis into the field of sEMG process to investigate the hidden nonlinear characteristics of sEMG of patients with facial paralysis. sEMG on the bilateral masseter, levator labii superioris and frontalis of the 21 patients is recorded. Chaotic analysis is employed to extract new features, including correlation dimension, Lyapunov exponent, approximate entropy and so on. We discover the maximum Lyapunov exponents are all greater than 0, indicating that sEMG is a chaotic signal; correlation dimensions of sEMG on healthy sides are all smaller than that of diseased sides; and inversely, the approximate entropies of healthy sides are all greater than that of diseased sides. Consequently, chaotic analysis can provide a new insight into the complexity of the EMG and may be a vital indicator of diagnosis and recovery assessment of facial paralysis.

Keywords

sEMG chaotic analysis features extraction facial paralysis 

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References

  1. 1.
    Tang, X.F.: Clinical electromyography. The Joint Publishing House of Beijing University of Science and Technology & China Union Medical University, Beijing (1995)Google Scholar
  2. 2.
    Bilodeau, M., Schindler-Ivens, S., Williams, D.M., et al.: EMG frequency content changes with increasing force and during fatigue in the quadriceps femoris muscle of men and women. Journal of Electromyography and Kinesiology 13(1), 83–92 (2003)CrossRefGoogle Scholar
  3. 3.
    Chan, A.D.C., Englehart, K.B.: Continuous classification of myoelectric signals for powered prostheses using Gaussian mixture models. In: IEEE 25th Annual International Conference, vol. 3, pp. 2841–2844 (2003)Google Scholar
  4. 4.
    Frigo, C., Ferrarin, M., Frasson, W., et al.: EMG signals detection and processing for on-line control of functional electrical stimulation. Journal of Electromyography and Kinesiology 10(5), 351–360 (2000)CrossRefGoogle Scholar
  5. 5.
    Graupe, D., Cline, W.K.: Function separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Trans. on Syst., Man, Cyber., SMC-5(2), 252–259 (1975)Google Scholar
  6. 6.
    Chen, X., Zhang, X., Zhao, Z.Y., et al.: Multiple Hand Gesture Recognition based on Surface EMG Signal. In: 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 506–509 (2007)Google Scholar
  7. 7.
    Horiuchi, Y., Kishi, T., Gonzalez, J., et al.: A Study on Classification of Upper Limb Motions from Around-Shoulder Muscle Activities. In: IEEE 11th International Conference on Rehabilitation Robotics, pp. 311–315 (2009)Google Scholar
  8. 8.
    Doud, J.R., Walsh, J.M.: Muscle fatigue and muscle length interaction: effect on the EMG frequency components. Electromyography and Clinical Neurophysiology 35(6), 331–339 (1995)Google Scholar
  9. 9.
    Mix, D.F., Olejniczak, K.J.: Elements of Wavelets for Engineers and Scientists. John Wiley & Sons, Inc. (2003)Google Scholar
  10. 10.
    Saito, N., Coifman, R.R.: Local discriminant bases and their applications. J. Math. Imag. Vis. 5(4), 337–358 (1995)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Boostani, R., Moradi, M.H.: Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological Measurement 24, 309–319 (2003)CrossRefGoogle Scholar
  12. 12.
    Englehart, K., Hudgins, B., Parker, P.A.: A Wavelet-Based Continuous Classification Scheme for Multifunction Myoelectric Control. IEEE Trans on Biomedical Engineering 48(3), 302–311 (2001)CrossRefGoogle Scholar
  13. 13.
    Chu, J.U., Moon, I., Lee, Y.J., et al.: A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control. IEEE/ASME Trans. on Mechatronics 12(3), 282–290 (2007)CrossRefGoogle Scholar
  14. 14.
    Chen, W.T.: A Study of Feature Extraction from sEMG Singal Based on Entropy, A Dissertation for the Degree of Doctor of Philosophy, Biomedical Engineering. Shanghai Jiao Tong University (2008)Google Scholar
  15. 15.
    Ott, E.: Chaos in Dynamical Systems, 2nd edn. Cambridge University Press, Cambridge (September 9, 2002)Google Scholar
  16. 16.
    Han, C.X.: Research on the Acupuncture Neural Electrical Signals Conduction and Effect, A Dissertation for the Degree of Doctor of Philosophy, Control Science and Engineering. Tianjin University (2010)Google Scholar
  17. 17.
    Chen, W.T., Wang, Z.Z., Ren, X.M.: Characterization of Surface EMG Signals Using Improved Approximate Entropy. Journal of Zhejiang University Science B 7, 844–848 (2006)CrossRefGoogle Scholar
  18. 18.
    Bodruzzaman, M., Zein-Sabatto, S., Marpaka, D., et al.: Neural network-based classification of electromyographic (EMG) signal during dynamic muscle contraction. In: IEEE Southeastcon 1992, April 12-15, pp. 99–102 (1992)Google Scholar
  19. 19.
    Bodruzzaman, M., Cadzow, J., Shiavi, R., et al.: Hurst’s rescaled-range (R/S) analysis and fractal dimension of electromyographic (EMG) signal. In: IEEE Southeastcon 1991, April 7-10, pp. 1121–1123 (1991)Google Scholar
  20. 20.
    Bodruzzaman, M., Devgan, S., Kari, S.: Chaotic classification of electromyographic (EMG) signals via correlation dimension measurement. In: IEEE Southeastcon 1992, pp. 95–98 (1992)Google Scholar
  21. 21.
    Erfanian, A., Chizeck, H.J., Hashemi, R.M.: Chaotic activity during electrical stimulation of paralyzed muscle. In: IEEE 18th Annual International Conference, vol. 4, pp. 1756–1757 (1997)Google Scholar
  22. 22.
    Ehtiati, T., Kinsner, W., Moussavi, Z.K.: Multifractal characterization of the electromyogram signals in presence of fatigue. In: IEEE Canadian Conference on Electrical and Computer Engineering, May 24-28, vol. 2, pp. 866–869 (1998)Google Scholar
  23. 23.
    Small, G.J., Jones, N.B., Fothergill, J.C., et al.: Chaos as a possible model of electromyographic activity. In: IEEE Intl. Conf. on Simulation, pp. 27–34 (September 1998)Google Scholar
  24. 24.
    Lei, M., Wang, Z.Z., Feng, Z.J.: The application of symplectic geometry on nonlinear dynamics analysis of the experimental data. In: 14th International Conference on Digital Signal, pp. 1137–1140 (2002)Google Scholar
  25. 25.
    Padmanabhan, P., Puthusserypady, S.: Nonlinear analysis of EMG signals - a chaotic approach. In: IEEE Conf. on Eng. Med. Biol. Soc., vol. 1, pp. 608–611 (2004)Google Scholar
  26. 26.
    Liu, C., Wang, X.: Recurrence quantification analysis of electrically evoked surface EMG signal. In: Conf. on Eng. Med. Biol. Soc., vol. 5, pp. 4572–4575 (2005)Google Scholar
  27. 27.
    Zhang, X., Chen, X., Barkhaus, P.E., et al.: Multiscale Entropy Analysis of Different Spontaneous Motor Unit Discharge Patterns. IEEE Journal of Biomedical and Health Informatics 17(2), 470–476 (2013)CrossRefGoogle Scholar
  28. 28.
    Gallez, D., Babloyantz, A.: Predictability of human EEG: a dynamical approach. Biol. Cvbern. 64(5), 381–391 (1991)CrossRefGoogle Scholar
  29. 29.
    Ashwin, P.: Nonlinear dynamics: Synchronization from chaos. Nature 422(6930), 384–385 (2003)CrossRefGoogle Scholar
  30. 30.
    Grassberger, P., Procaccia, I.: Characterization of Strange Attractors. Phys. Rev. Lett. 50, 346–349 (1983)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Abarbanel, H.D.I., Brown, R., Sidorowich, J.J., et al.: The analysis of observed chaotic data in physical systems. Rev. Mod. Phys. 65, 1331–1392 (1993)CrossRefMathSciNetGoogle Scholar
  32. 32.
    Cao, L.Y.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena 110(1-2), 43–50 (1997)CrossRefMATHGoogle Scholar
  33. 33.
    Felici, F., Rosponi, A., Sbriccoli, P., et al.: Linear and non-linear analysis of surface electromyograms in weightlifters. European Journal of Applied Physiology 84(4), 337–342 (2001)CrossRefGoogle Scholar
  34. 34.
    Luo, Z.Z., Yang, G.Y.: Prosthetic Hand Fuzzy Control Based on Touch and Myoelectric Signal. Robot 28(2), 224–228 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anbin Xiong
    • 1
    • 2
  • Xingang Zhao
    • 1
  • Jianda Han
    • 1
  • Guangjun Liu
    • 1
    • 3
  1. 1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA)Chinese Academy of Sciences (CAS)ShenyangChina
  2. 2.University of Chinese Academy of Sciences (CAS)BeijingChina
  3. 3.Department of Aerospace EngineeringRyerson UniversityTorontoCanada

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