Multifractal analysis of sEMG signal of the complex muscle activity

The neuro--muscular activity while working on laparoscopic trainer is the example of the complex (and complicated) movement. This class of problems are still waiting for the proper theory which will be able to describe the actual properties of the muscle performance. Here we consider the signals obtained from three states of muscle activity: at maximum contraction, during complex movements (at actual work) and in the completely relaxed state. In addition the difference between a professional and an amateur is presented. The Multifractal Detrended Fluctuation Analysis was used in description of the properties the kinesiological surface electromyographic signals (sEMG). Based on the results obtained in the form of multifractal spectra together with the parameters which effectively describes it, like the spectrum half--width, or the Hurst or the singularity exponents, we demonstrate the dissimilarity between each state of work for the selected group of muscles as well as between trained and untrained individuals. For the well-trained person (professional) at work mf-spectrum shows similarity with the relaxed state, i.e. the spectrum will be truncated at the right side which show the dominance of the low fluctuations. On the contrary the spectrum for the untrained person at actual work will tend to be rather broad and symmetric. This feature hidden in the sEMG fluctuations allows for the determination of the level of training not only in the case of surgeons but also opens a possibility for similar analysis in any other complex motion with the use of the noninvasive surface electromyography.

[1]  Dinesh Kant Kumar,et al.  Computation of fractal features based on the fractal analysis of surface Electromyogram to estimate force of contraction of different muscles , 2014, Computer methods in biomechanics and biomedical engineering.

[2]  Boris Podobnik,et al.  Detrended cross-correlation analysis for non-stationary time series with periodic trends , 2011 .

[4]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[5]  E. Bacry,et al.  Multifractal formalism for fractal signals: The structure-function approach versus the wavelet-transform modulus-maxima method. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[6]  Pornchai Phukpattaranont,et al.  Fractal analysis features for weak and single-channel upper-limb EMG signals , 2012, Expert Syst. Appl..

[7]  Tanu Sharma,et al.  A novel feature extraction for robust EMG pattern recognition , 2016, Journal of medical engineering & technology.

[8]  C. Cescon,et al.  Non-invasive assessment of the gracilis muscle by means of surface electromyography electrode arrays. , 2006, The Journal of surgical research.

[9]  Espen A. F. Ihlen,et al.  Introduction to Multifractal Detrended Fluctuation Analysis in Matlab , 2012, Front. Physio..

[10]  R Merletti,et al.  Surface EMG: the issue of electrode location. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[11]  Guruprasad Madhavan,et al.  Electromyography: Physiology, Engineering and Non-Invasive Applications , 2005, Annals of Biomedical Engineering.

[12]  B. Mandelbrot Multifractals And 1/F Noise , 1999 .

[13]  Joanna Wdowczyk-Szulc,et al.  Reading multifractal spectra: Aging by multifractal analysis of heart rate , 2011 .

[14]  H E Stanley,et al.  Statistical physics and physiology: monofractal and multifractal approaches. , 1999, Physica A.

[15]  胡晓,et al.  Classification of surface EMG signal with fractal dimension , 2005 .

[16]  Ivanov PCh,et al.  Stochastic feedback and the regulation of biological rhythms. , 1997, Europhysics letters.

[17]  Jaroslaw Kwapien,et al.  INVESTIGATING MULTIFRACTALITY OF STOCK MARKET FLUCTUATIONS USING WAVELET AND DETRENDING FLUCTUATION METHODS , 2005 .

[18]  K. Sreenivasan FRACTALS AND MULTIFRACTALS IN FLUID TURBULENCE , 1991 .

[19]  Benoit B. Mandelbrot,et al.  Multifractals and 1/f noise : wild self-affinity in physics (1963-1976) : selecta volume N , 1999 .

[20]  A. Phinyomark,et al.  Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification , 2011 .

[21]  Françoise Argoul,et al.  Multifractal analysis of dynamic infrared imaging of breast cancer , 2013 .

[22]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[23]  The Application of Econophysics , 2012 .

[24]  H. Stanley,et al.  Multifractal phenomena in physics and chemistry , 1988, Nature.

[25]  Dinesh Kumar,et al.  Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review , 2011, Australasian Physical & Engineering Sciences in Medicine.

[26]  V. Carbone,et al.  Observation of the multifractal spectrum at the termination shock by Voyager 1 , 2011 .

[27]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[28]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[29]  A. Kolmogorov The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers , 1991, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

[31]  H. Stanley,et al.  A multifractal analysis of Asian foreign exchange markets , 2008 .

[32]  Jan W. Kantelhardt Fractal and Multifractal Time Series , 2009, Encyclopedia of Complexity and Systems Science.