Multifractal Analysis of Electromyography Data



Myopathies (MYO) are a group of disorders where malfunction of muscle fibers occurs for a number of reasons which results in a muscular dysfunction manifesting weakness of muscles. Neuropathies are also disorders of the peripheral nervous system for which information transmission from brain and spinal cord to every other part of the body is disturbed. For diagnosis and characterization of motor neuron disease (MND), myopathy, and neuropathy, the electromyography (EMG) is extensively used since EMG signal can be analyzed to obtain information in regard to degree of disorder. The contents of the chapter deal with the details of a rigorous and robust non-linear technique, namely, multifractal detrended fluctuation analysis, to assess the multifractal property of EMG signals of patients with neuromuscular disorders and also use of two quantitative parameters, the multifractal width, and the auto-correlation exponent as biomarker for diagnosis and prognosis of both MYO and NEURO and even for early detection of MND.


  1. Acharya UR, Ng EYK, Swapna G, Michelle YSL (2011) Classification of normal, neuropathic, and myopathic electromyography signals using non-linear dynamics method. J Med Imaging Health Inform 1:375–380CrossRefGoogle Scholar
  2. Alamedine D, Khalil M, Marque C (2013) Comparison of different EHG feature selection methods for the detection of preterm labo. Comput Math Method Med 2013:585–593CrossRefGoogle Scholar
  3. Alkan A, Gunay M (2012) Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 39:44–47CrossRefGoogle Scholar
  4. Ancillao A, Galli M, Rigoldi C, Albertini G (2014) Linear correlation between fractal dimension of surface EMG signal from rectus femoris and height of vertical jump. Chaos Solitons Fractals 66:120–126CrossRefGoogle Scholar
  5. Anmuth CJ, Goldberg G, Mayer NH (1994) Fractal dimension of EMG signals recorded with surface electrodes during isometric contractions is linearly correlated with muscle activation. Muscle Nerve 17:953–954PubMedCrossRefPubMedCentralGoogle Scholar
  6. Arjunan SP, Kumar DK (2007). Fractal theory based non-linear analysis of SEMG. In: IEEE 3rd international conference on Intelligent Sensors, Sensor Networks and Information, 3–6 December 2007, pp 545–548Google Scholar
  7. Arjunan SP, Kumar DK (2014) Computation of fractal features based on the fractal analysis of surface electromyogram to estimate force of contraction of different muscles. Comput Methods Biomech Biomed Engin 17:210–216CrossRefGoogle Scholar
  8. Artameeyanant P, Sultornsanee S, Chamnongthai K (2016) An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection. Springer Plus 5:2101PubMedCrossRefPubMedCentralGoogle Scholar
  9. Artemiadis PK, Kyriakopoulos KJ (2007) EMG-based teleoperation of a robot arm using low-dimensional representation. In: Proceedings of IEEE/RSJ international conference on Intelligent Robots and Systems, 29 October – 2 November 2007, pp 489–495Google Scholar
  10. Artemiadis PK, Kyriakopoulos KJ (2010) EMG-based control of a robot arm using low-dimensional embeddings. IEEE Trans Robot 26:393–398CrossRefGoogle Scholar
  11. Artemiadis PK, Kyriakopoulos KJ (2011) A switching regime model for the EMG-based control of a robot arm. IEEE Trans Syst Man Cybern 41:53–63CrossRefGoogle Scholar
  12. Ashkenazy Y, Havlin S, Ivanov PC, Peng CK, Frohlinde VS et al (2003a) Magnitude and sign scaling in power-law correlated time-series. Physica A 323:19–41CrossRefGoogle Scholar
  13. Ashkenazy Y, Baker DR, Gildor H, Havlin S (2003b) Non-linearity and multifractality of climate change in the past 420,000 years. Geophys Res Lett 30:2146–2149CrossRefGoogle Scholar
  14. Basmajian J, De Luca CJ (1985) Muscles alive: their functions revealed by electromyography, 5th edn. Williams & Wilkins, BaltimoreGoogle Scholar
  15. Bue BD, Merényi E, Killian JM (2013) Classification and diagnosis of myopathy from EMG signals. In: 2nd workshop on data mining for medicine and healthcare, in conjunction with the 13th SIAM international conference on Data Mining (SDM-DMMH), Austin, TX, May 2013Google Scholar
  16. Chang YC, Chang S (2002) A fast estimation algorithm on the Hurst parameter of discrete-time fractional Brownian motion. IEEE Trans Signal Process 50:554–559CrossRefGoogle Scholar
  17. Chang GC, Kang WJ, Luh JJ, Cheng CK, Lai JS et al (1996) Real-time implementation of electromyogram pattern recognition as a control command of man–machine interface. Med Eng Phys 18:529–537PubMedCrossRefPubMedCentralGoogle Scholar
  18. Chang S, Mao ST, Hu SJ, Lin WC, Cheng CL (2000) Studies of detrusorsphincter synergia and dyssynergia during micturition in rats via fractional Brownian motion. IEEE Trans Biomed Eng 47:1066–1073PubMedCrossRefPubMedCentralGoogle Scholar
  19. Chang S, Hu SJ, Lin WC (2004) Fractal dynamics and synchronization of rhythms in urodynamics of female Wistar rats. J Neurosci Methods 139:271–279PubMedCrossRefPubMedCentralGoogle Scholar
  20. Chang S, Li SJ, Chiang MJ, Hu SJ, Hsyu MC (2007) Fractal dimension estimation via spectral distribution function and its application to physiological signals. IEEE Trans Biomed Eng 54:1895–1898PubMedCrossRefGoogle Scholar
  21. Chen B, Wang N (2000) Determining EMG embedding and fractal dimensions and its application. In: Proceedings of the 22nd annual EMBS international conference, Chicago IL, USA, pp 1341–1344Google Scholar
  22. Chen W, Wang Z, Ren X (2006) Characterization of surface EMG signals using improved approximate entropy. J Zhejiang Univ Sci B 7:844–848PubMedPubMedCentralCrossRefGoogle Scholar
  23. Chhabra A, Jensen RV (1989) Direct determination of the f(α) singularity spectrum. Phys Rev Lett 62:1327–1330CrossRefGoogle Scholar
  24. Dang KTQ, Minh HL, Thanh HN, Van TV (2012) Analyzing surface EMG signals to determine relationship between jaw imbalance and arm strength loss. Biomed Eng Online 11:55CrossRefGoogle Scholar
  25. Diab A, El-Merhie A, El-Halabi N, Khoder L (2010) Classification of uterine EMG signals using supervised classification method. Biomed Sci Eng 3:837–842CrossRefGoogle Scholar
  26. Diab A, Hassan M, Marque C, Karlsson B (2012) Quantitative performance analysis of four methods of evaluating signal non-linearity: application to uterine EMG signals. In: Proceedings of annual international conference of the IEEE Engineering in Medicine and Biology Society, pp 1045–1048Google Scholar
  27. Easwaramoorthy D, Uthayakumar R (2011) Improved generalized fractal dimensions in the discrimination between healthy and epileptic EEG signals. J Comput Sci 2:31–38CrossRefGoogle Scholar
  28. Eke A, Herman P, Kocsis L, Kozak LR (2002) Fractal characterization of complexity in temporal physiological signals. Physiol Meas 23:1–38CrossRefGoogle Scholar
  29. Falconer K (2003) Fractal geometry. Wiley, New York, p 337CrossRefGoogle Scholar
  30. Farina D, Negro F (2012) Accessing the neural drive to muscle and translation to neurorehabilitation technologies. IEEE Rev Biomed Eng 5:3–14PubMedCrossRefPubMedCentralGoogle Scholar
  31. Farina D, Merletti R, Nazzaro M, Caruso I (2001) Effect of joint angle on EMG variables in leg and thigh muscles. IEEE Eng Med Biol Mag 20:62–71PubMedCrossRefPubMedCentralGoogle Scholar
  32. Fele GF, Kavsek G, Novak ZA, Jager F (2008) A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and preterm delivery groups. Med Biol Eng Comput 46:911–922CrossRefGoogle Scholar
  33. Fox CG (1989) Empirically derived relationships between fractal dimension and power law form frequency spectra. Fractals Geophy (Part Pure Appl Geophy) 131:211–239CrossRefGoogle Scholar
  34. Fuglsang-Frederiksen A (2000) The utility of interference pattern analysis. Muscle Nerve 23:18–36PubMedCrossRefPubMedCentralGoogle Scholar
  35. Gabriel DA, Kamen G (2009) Experimental and modeling investigation of spectral compression of biceps brachii SEMG activity with increasing force levels. J Electromyogr Kinesiol 19:437–448PubMedCrossRefPubMedCentralGoogle Scholar
  36. Gang W, Xiao-Mei R, Lei L, Zhi-Zhong W (2007) Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions. J Zheijang Univ Sci A 8:910–915CrossRefGoogle Scholar
  37. Gerdle B, Eriksson N (1990) The behavior of mean power frequency of the surface electromyogram in Biceps brachii with increasing force and during fatigue with special regard to electrode distance. J Electromyogr Neurophysiol 30:483–489Google Scholar
  38. Ghosh D, Dutta S, Chakraborty S, Samanta S (2017) Chaos based quantitative electro-diagnostic marker for diagnosis of myopathy, neuropathy and motor neuron disease. J Neurol Neurosci 8:226CrossRefGoogle Scholar
  39. Gitiaux C, Chemaly N, Quijano-Roy S, Barnerias C, Desguerre I et al (2016) Motor neuropathy contributes to crouching in patients with Dravet syndrome. Neurology 87:277–281PubMedCrossRefPubMedCentralGoogle Scholar
  40. Gitter JA, Czerniecki MJ (1995) Fractal analysis of electromyographic interference pattern. J Neurosci Methods 58:103–108PubMedCrossRefPubMedCentralGoogle Scholar
  41. Goen A (2014) Classification of EMG signals for assessment of neuromuscular disorders. Int J Electron Electr Eng 2:242–248CrossRefGoogle Scholar
  42. Goge A, Chan A (2004) Investigating classification parameters for continuous myoelectrically controlled prostheses. In: Proceedings of the 28th conference of the Canadian Medical & Biological Engineering Society, pp 141–144Google Scholar
  43. Gokgoz E, Subasi A (2015) Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed Signal Process Control 18:138–144CrossRefGoogle Scholar
  44. Gupta V, Suryanarayanan S, Reddy NP (1997) Fractal analysis of surface EMG signals from the biceps. Int J Med Inform 45:185–192PubMedCrossRefPubMedCentralGoogle Scholar
  45. Hassan M, Terrien J, Karlsson C (2007) Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals. Med Eng Phys 33:980–986CrossRefGoogle Scholar
  46. Hassan M, Alexandersson M, Terrien J, Muszynski C, Marque C (2012) Better pregnancy monitoring using non-linear correlation analysis of external uterine electromyography. IEEE Trans Biomed Eng 60:1160–1166PubMedCrossRefPubMedCentralGoogle Scholar
  47. Hu X, Wang ZZ, Ren XM (2005) Classification of surface EMG signal with fractal dimension. J Zhejiang Univ Sci B 6:844–848PubMedPubMedCentralCrossRefGoogle Scholar
  48. Huang XY, Schmitt FG, Hermand JP, Gagne Y, Lu ZM (2011) Arbitrary order Hilbert spectral analysis for time series possessing scaling statistics: comparison study with detrended fluctuation analysis and wavelet leaders. Phys Rev E 84:016208–016213CrossRefGoogle Scholar
  49. Hudgins B, Parker P, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40:82–94PubMedCrossRefPubMedCentralGoogle Scholar
  50. Janjarasjitt S (2014) Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data. J Zhejiang Univ Sci C 15:1147–1153CrossRefGoogle Scholar
  51. Jezewski J, Horoba K, Matonia A, Wrobel J (2005) Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach. Physiol Meas 26:753–767PubMedCrossRefGoogle Scholar
  52. Kang WJ, Cheng CK, Lai JS, Shiu JR, Kuo TS (1996) A comparative analysis of various EMG pattern recognition methods. Med Eng Phys 18:390–395PubMedCrossRefGoogle Scholar
  53. Kantelhardt JW, Zschiegner SA, Koscielny BE, Havlin S, Bunde A et al (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316:87–114CrossRefGoogle Scholar
  54. Kantelhardt JW, Rybski D, Zschiegner SA, Braun P, Bunde EK et al (2003) Multifractality of river runoff and precipitation: comparison of fluctuation analysis and wavelet methods. Physica A 330:240–245CrossRefGoogle Scholar
  55. Katz M (1988) Fractals and the analysis of waveforms. Comput Biol Med 18:145–156PubMedCrossRefPubMedCentralGoogle Scholar
  56. Khalil M, Duchene J (2007) Uterine EMG analysis: a dynamic approach for change detection and classification. IEEE Trans Biomed Eng 47:748–756CrossRefGoogle Scholar
  57. Kincaid JC (2015) Nerve conduction studies and needle EMG. In: Nerves and nerve injuries, vol 1. Elsevier, London, pp 125–145Google Scholar
  58. Koike Y, Kawato M (1995) Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biol Cybern 73:291–300PubMedCrossRefPubMedCentralGoogle Scholar
  59. Kupa EJ, Roy SH, Kandarian SC, de Luca CJ (1995) Effects of muscle fiber type and size on EMG median frequency and conduction velocity. J Appl Physiol 79:23–32PubMedCrossRefPubMedCentralGoogle Scholar
  60. Lei M, Meng G (2012) Non-linear analysis of surface EMG signals. In: Naik GR (ed) Computational intelligence in electromyography analysis – a perspective on current applications and future challenges. Intech Open, pp 119–174Google Scholar
  61. Lima CAM, Coelho A, Madeo RCB, Peres SM (2016) Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Comput Appl 27:791–804CrossRefGoogle Scholar
  62. Lindstrom L, Kadefors R, Petersen I (1977) An electromyographic index for localized muscle fatigue. J Appl Physiol Respir Environ Exerc Physiol 43:750–754PubMedGoogle Scholar
  63. Lucovnik M, Maner LW, Chambliss LR, Blumrick R, Balducci R et al (2011) Noninvasive uterine electromyography for prediction of preterm delivery. Am J Obstet Gynecol 204:228–2e1PubMedCrossRefGoogle Scholar
  64. Marri K, Swaminathan R (2015a) Analyzing origin of multifractality of surface electromyography signals in dynamic contractions. J Nanotechnol Eng Med 6:031002–031001CrossRefGoogle Scholar
  65. Marri K, Swaminathan R (2015b) Identification of onset of fatigue in biceps Brachii muscles using surface EMG and multifractal DMA alogrithm. Biomed Sci Instrum 51:107–114PubMedGoogle Scholar
  66. Marri K, Swaminathan R (2016) Analysis of biceps Brachii muscles in dynamic contraction using sEMG signals and multifractal DMA algorithm. Int J Signal Process Syst 4:79–85Google Scholar
  67. McArthur L, Mackenzie S, Boland J (2013) Multifractal analysis of wind farm power output. In: 20th international Congress on Modeling and Simulation (MODSIM 2013), Adelaide, Australia, 1–6 December 2013, pp 420–426Google Scholar
  68. Mishra VK, Bajaj V, Kumar A, Singh GK (2016) Analysis of ALS and normal EMG signals based on empirical mode decomposition. IET Sci Measurement Technol 10:963–971CrossRefGoogle Scholar
  69. Monsifrot J, Carpentier EL, Aoustin Y (2004) Sequential decoding of intramuscular EMG signals via estimation of a Markov model. IEEE Trans Neural Syst Rehabil Eng 22:1030–1038CrossRefGoogle Scholar
  70. Moreside JM, Quirk DA, Hubley-Kozey CL (2014) Temporal patterns of the trunk muscles remain altered in a low back-injured population despite subjective reports of recovery. Arch Phys Med Rehabil 95:686–698PubMedCrossRefPubMedCentralGoogle Scholar
  71. Naeem SM, Seddik AF, Eldosoky MA (2014) New technique based on uterine electromyography non-linearity for preterm delivery detection. J Eng Technol Res 6:107–114Google Scholar
  72. Naik G, Kumar D, Arjunan S (2009) Use of SEMG in identification of low level muscle activities: features based on ICA and fractal dimension. Conf Proc IEEE Eng Med Biol Soc 2009:364–367PubMedPubMedCentralGoogle Scholar
  73. Naik GR, Selvan SE, Nguyen HT (2016) Single-channel EMG classification with ensemble-empirical-mode-decomposition- based ICA for diagnosing neuromuscular disorders. IEEE Trans Neural Syst Rehabil Eng 24:734–743PubMedCrossRefPubMedCentralGoogle Scholar
  74. Najarian K, Splinter R (2012) Biomedical signal and image processing, 2nd edn. CRC Press/Taylor & Francis Group, Boca Raton/London/New YorkGoogle Scholar
  75. Nikolic M, Krarup C (2011) EMGTools, an adaptive and versatile tool for detailed EMG analysis. IEEE Trans Biomed Eng 58:2707–2718PubMedCrossRefPubMedCentralGoogle Scholar
  76. Nussbaum MA, Yassierli (2003) Assessment of localized muscle fatigue furing low-moderate static contractions using the fractal dimension of EMG. In: Proceedings of the XVth triennial Congress of the International Ergonomics Association, Seoul, Korea, August 25–29Google Scholar
  77. Oswiecimka P, Kwapien J, Drozdz S (2006) Wavelet versus detrended fluctuation analysis of multifractal structures. Phys Rev E 74:06103–06137CrossRefGoogle Scholar
  78. Patidar M, Jain N, Parikh A (2013) Classification of normal and myopathy EMG signals using BP neural network. Int J Comput Appl 69:0975–8887Google Scholar
  79. Peng CK, Buldyrev SV, Havlin S, Simons M, Stanley HE et al (1994) Mosaic organization of DNA nucleotides. Phys Rev E 49:1685–1689CrossRefGoogle Scholar
  80. Phinyomark A, Phothisonothai M, Limsakul C, Phukpattaranont P (2009) Detrended fluctuation analysis of electromyography signal to identify hand movement. In: Proceedings of the second Biomedical Engineering international conference, Phuket, Thailand, August 13–14, 2009, pp 324–329Google Scholar
  81. Phinyomark A, Phothisonothai M, Limsakul C, Phukpattaranont P (2010) Effect of trends on detrended fluctuation analysis for surface electromyography (EMG) signal. In: The eighth PSU Engineering conference 22–23 April 2010, pp 333–338Google Scholar
  82. Phinyomark A, Phukpattaranont P, Limsakul C (2012) Fractal analysis features for weak and single-channel upper-limb EMG signals. Expert Syst Appl 39:11156–11163CrossRefGoogle Scholar
  83. Ravier P, Buttelli O, Jennane R, Couratier P (2005) An EMG. Fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions. J Electromyogr Kinesiol 15:210–221PubMedCrossRefPubMedCentralGoogle Scholar
  84. Ren P, Yao S, Li J, Valdes-Sosa PA, Kendrick KM (2015) Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals. PLoS One 10:e0132116PubMedPubMedCentralCrossRefGoogle Scholar
  85. Riillo F, Quitadamo L, Cavrinia F, Gruppioni E, Pinto C et al (2014) Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees. Biomed Signal Process Control 14:117–125CrossRefGoogle Scholar
  86. Ryu W, Han B, Kim J (2008) Continuous position control of 1 DOF manipulator using EMG signals. In: Proceeding in 3rd international conference on Convergence and Hybrid Information Technology, 11–13 November, 2008, pp 870–874Google Scholar
  87. Sarkar M, Leong TY (2003) Characterization of medical time series using fuzzy similarity-based fractal dimensions. Artif Intell Med 27:201–222PubMedCrossRefPubMedCentralGoogle Scholar
  88. Serrano E, Figliola A (2009) Wavelet leaders: a new method to estimate the multifractal singularity spectra. Physica A 388:2793–2805CrossRefGoogle Scholar
  89. Sharma RR, Chandra P, Pachori RB (2017) Electromyogram signal analysis using eigenvalue decomposition of the Hankel Matrix. In: International conference on machine intelligence and signal processing at: Indian Institute of Technology Indore, Indore, India, November, 2017Google Scholar
  90. Shields R (2006) Fractal dimension of the EMG interference pattern: preliminary observations and comparisons with other measures of interference pattern analysis. J Clin Neurophysiol 10:117–118Google Scholar
  91. Shimizu Y, Thurner S, Ehrenberger K (2002) Multifractal spectra as a measure of complexity in human posture. Fractals 10:103–116CrossRefGoogle Scholar
  92. Smith RJ, Tenore F, Huberdeau D, Etienne-Cummings R, Thakor NV (2008) Continuous decoding of finger position from surface EMG signals for the control of powered prostheses. In: Proceedings of IEEE 30th annual international Conference on Engineering in Medicine and Biology Society, August 2008, pp 197–200Google Scholar
  93. Stulen FB, De Luca CJ (1981) Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans Biomed Eng 28:515–523PubMedCrossRefGoogle Scholar
  94. Subasi A (2013) Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43:576–586PubMedCrossRefGoogle Scholar
  95. Talebinejad M, Chan ADC, Miri A, Dansereau RM (2009) Fractal analysis of surface electromyography signals: a novel power spectrum-based method. J Electromyogr Kinesiol 19:840–850PubMedCrossRefGoogle Scholar
  96. Trojaborg W (1987) Motor unit disorders and myopathies. In: Halliday MA, Butler RJ, Paul R (eds) A textbook book of clinical neurophysiology. Wiley, New York, pp 417–438Google Scholar
  97. Vishnu RS, Shalu GK (2015) Identification of surface EMG – angular velocity model using artificial neural network. Int J Adv Res Electr Electron Instrument Eng 4:7201–7208CrossRefGoogle Scholar
  98. Wang G, Ren X, Li L, Wang Z (2007) Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions. J Zheijang Univ Sci A 8:910–915CrossRefGoogle Scholar
  99. Webber CL Jr, Schmidt MA, Walsh JM (1995) Influence of isometric loading on biceps EMG dynamics as assessed by linear and non-linear tools. J Appl Physiol 78:814–822PubMedCrossRefPubMedCentralGoogle Scholar
  100. Weiss JM, Weiss LD, Silver JK (2015) Neuromuscular junction disorders, easy EMG: a guide to performing nerve conduction studies and electromyography. Elsevier, London. ISBN:978-0-323-28664-0Google Scholar
  101. Xu Z, Xiao S (1997) Fractal dimension of surface EMG and its determinants. In: Proceedings of 19th international conference – IEEE/EMBS, Chicago, IL, USA, pp 1570–1573Google Scholar
  102. Zhao J, Jiang L, Cai H, Liu H (2007) EMG pattern recognition method for prosthetic hand based on wavelet transform and sample entropy. Control Decis 22:927–930Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of PhysicsSir C V Raman Centre for Physics and Music, Jadavpur UniversityKolkataIndia
  2. 2.Department for PhysicsSeacom Engineering CollegeHowrahIndia
  3. 3.Electrical and Electronics EngineeringICFAI UniversityAgartalaIndia

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