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

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.

[1]  Xu Wang,et al.  Recurrence quantification analysis of electrically evoked surface EMG signal , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[2]  N. B. Jones,et al.  Chaos as a possible model of electromyographic activity , 1998 .

[3]  Mohammad Hassan Moradi,et al.  Evaluation of the forearm EMG signal features for the control of a prosthetic hand. , 2003, Physiological measurement.

[4]  Witold Kinsner,et al.  Multifractal characterization of the electromyogram signals in presence of fatigue , 1998, Conference Proceedings. IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.98TH8341).

[5]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[6]  E. Ott.  Chaos in Dynamical Systems: Contents , 2002 .

[7]  M. Bilodeau,et al.  EMG frequency content changes with increasing force and during fatigue in the quadriceps femoris muscle of men and women. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[8]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[9]  G. C. Filligoi,et al.  Linear and non-linear analysis of surface electromyograms in weightlifters , 2001, European Journal of Applied Physiology.

[10]  秦 浩起,et al.  Characterization of Strange Attractor (カオスとその周辺(基研長期研究会報告)) , 1987 .

[11]  Howard Jay Chizeck,et al.  Chaotic activity during electrical stimulation of paralyzed muscle , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  M. Ferrarin,et al.  EMG signals detection and processing for on-line control of functional electrical stimulation. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[13]  Peter Ashwin,et al.  Nonlinear dynamics: Synchronization from chaos , 2003, Nature.

[14]  Wenwei Yu,et al.  A study on classification of upper limb motions from around-shoulder muscle activities , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[15]  A. Babloyantz,et al.  Predictability of human EEG: a dynamical approach , 1991, Biological Cybernetics.

[16]  Kevin Englehart,et al.  Continuous classification of myoelectric signals for powered prostheses using gaussian mixture models , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[17]  L. Tsimring,et al.  The analysis of observed chaotic data in physical systems , 1993 .

[18]  Daniel Graupe,et al.  Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  J M Walsh,et al.  Muscle fatigue and muscle length interaction: effect on the EMG frequency components. , 1995, Electromyography and clinical neurophysiology.

[20]  Xu Zhang,et al.  Multiple Hand Gesture Recognition Based on Surface EMG Signal , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[21]  Dwight F. Mix,et al.  Elements of Wavelets for Engineers and Scientists: Mix/Elements of Wavelets , 2005 .

[22]  Zhi-zhong Wang,et al.  Characterization of surface EMG signals using improved approximate entropy , 2006, Journal of Zhejiang University SCIENCE B.

[23]  E. Kugelberg,et al.  Clinical electromyography. , 1953, Progress in neurology and psychiatry.

[24]  M. Bodruzzaman,et al.  Neural network-based classification of electromyographic (EMG) signal during dynamic muscle contraction , 1992, Proceedings IEEE Southeastcon '92.

[25]  Ronald R. Coifman,et al.  Local discriminant bases and their applications , 1995, Journal of Mathematical Imaging and Vision.

[26]  James A. Cadzow,et al.  Hurst's rescaled-range (R/S) analysis and fractal dimension of electromyographic (EMG) signal , 1991, IEEE Proceedings of the SOUTHEASTCON '91.

[27]  Ping Zhou,et al.  Multiscale Entropy Analysis of Different Spontaneous Motor Unit Discharge Patterns , 2013, IEEE Journal of Biomedical and Health Informatics.

[28]  S. S. Devgan,et al.  Chaotic classification of electromyographic (EMG) signals via correlation dimension measurement , 1992, Proceedings IEEE Southeastcon '92.

[29]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[30]  Jean-Pierre Antoine.  Elements of Wavelets for Engineers and Scientists , 2005 .

[31]  Zhizhong Wang,et al.  The application of symplectic geometry on nonlinear dynamics analysis of the experimental data , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[32]  S. Puthusserypady,et al.  Nonlinear analysis of EMG signals - a chaotic approach , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  Shin-Ki Kim,et al.  A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control , 2007, IEEE/ASME Transactions on Mechatronics.

[34]  Luo Zhizeng.  Prosthetic Hand Fuzzy Control Based on Touch and Myoelectric Signal , 2006 .