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Characteristics of EMG activity in infants with movement disorders

Human Physiology volume 41, pages 39–46 (2015)Cite this article

Abstract

Neuromuscular characteristics of children without health problems and suffering from movement disorders have been studied by the method of surface electromyography. A total of 76 children aged from 6 months to 3 years have been examined: 61 children with movement disorders (13 of them with the ataxic form of cerebral palsy (CP), 48 with the spastic forms of CP) and 15 without movement disorders. The infant in a recumbent position has had their knee and hip joints passively flexed and extended. The electromyographic (EMG) activity of the basic muscle groups of the hip and shin was recorded. The EMG activity has been assessed using wavelet transform and subsequent analysis of the obtained time dependences. As a result, parameters related to diagnosis of infants with movement disorders have been revealed.

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References

  1. Badalyan, L.O. and Skvortsov, I.A., Klinicheskaya elektroneiromiografiya (Clinical Electroneuromyography), Moscow, 1986.

    Google Scholar 

  2. Nikolaev, S.G., Praktikum po klinicheskoi elektromiografii (Practical Course in Clinical Electromyography), Ivanovo, 2003.

    Google Scholar 

  3. Neckel, N., Nichols, D., Pelliccio, M., and Hidler, J., Abnormal synergy patterns and weakness in individuals with chronic stroke, J. Neuroeng. Rehabil., 2006, vol. 3, p. 17.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Lauer, R.T., Pierce, S.R., Tucker, C.A., Barbe, M.F., and Prosser, L.A., Age and electromyographic frequency alterations during walking in children with cerebral palsy, Gait Posture, 2010, no. 31 (1), p. 136.

    Google Scholar 

  5. Istenic, R., Kaplanis, P.A., Pattichis, C.S., and Zazula, D., Multiple entropy-based approach to automated surface EMG classification of neuromuscular disorders, Med. Biol. Eng. Comput., 2010, no. 48, p. 773.

    Google Scholar 

  6. Yusevich, Yu.S., Elektromiografiya v klinike nervnykh boleznei (Electromyography in Clinical Neurology), Moscow: MEDGIZ, 1958, p. 46.

    Google Scholar 

  7. Piper, H., Electrophysiologie Menschlicher Muskeln, Berlin: Springer, 1912.

    Book  Google Scholar 

  8. Beck, T.W., Stock, M.S., Defreitas, J.M., and Lynne-Davies, P., Shifts in EMG spectral power during fatiguing dynamic contractions, Muscle Nerve, 2014, vol. 50(1), p. 95.

    Article  PubMed  Google Scholar 

  9. Miura, T. and Sakuraba, K., Properties of force output and spectral EMG in young patients with nonspecific low back pain during isometric trunk extension, J. Phys. Ther. Sci., 2014, vol. 26(3), p. 323.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Merletti, R., Knaflitz, M., and DeLuca, C.J., Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions, J. Appl. Physiol., 1990, no. 68, p. 1657.

    Google Scholar 

  11. Zaripova, Yu.R. and Meigal, A.Yu., Nonlinear electromyographic parameters in infants with movement disorders, Fundam. Issled., 2012, no. 2, p. 58.

    Google Scholar 

  12. Akansu, A.N., Serdijn, W.A., and Selesnick, I.W., Emerging applications of wavelets: A review, Phys. Commun., 2010, vol. 3, no. 1, p. 1.

    Article  Google Scholar 

  13. Fairley, J.A., Georgoulas, G., Smart, O.L., Dimakopoulos, G., Karvelis, P., Stylios, C.D., Rye, D.B., and Bliwise, D.L., Wavelet analysis for detection of phasic electromyographic activity in sleep: influence of mother wavelet and dimensionality reduction, Comput. Biol. Med., 2014, no. 48, p. 77.

    Google Scholar 

  14. Zarjam, P., Epps, J., Chen, F., and Lovell, N.H., Estimating cognitive workload using wavelet entropy-based features during an arithmetic task, Comput. Biol. Med., 2013, no. 43 (12), p. 2186.

    Google Scholar 

  15. Kurenkov, A.L. and Sokolov, P.L., Electroneurophysiologic estimate of movement disorders during infantile cerebral palsy (literature review), Al’manakh Istselenie, 2001, no. 5, p. 6268.

    Google Scholar 

  16. Lauer, R.T., Stackhouse, C.A., Shewokis, P.A., Smith, B.T., Tucker, C.A., and McCarthy, J., A time-frequency based electromyographic analysis technique for use in cerebral palsy, Gait Posture, 2007, vol. 26(3), p. 420.

    Article  PubMed  Google Scholar 

  17. Potvin, J.R. and Brown, S.H.M., Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates, J. Electromyogr. Kinesiol., 2004, no. 14, p. 389.

    Google Scholar 

  18. Gorban, A.N. and Zinovyev, A.Y., Principal graphs and manifolds, in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, Hershey, 2009, p. 28.

    Google Scholar 

  19. Petersen, T.H., Farmer, S.F., Kliim-Due, M., and Nielsen, J.B., Failure of normal development of central drive to ankle dorsiflexors relates to gait deficits in children with cerebral palsy, J. Neurophysiol., 2013, vol. 109(3), p. 625.

    Article  PubMed  Google Scholar 

  20. Rose, J. and McGill, K.C., Neuromuscular activation and motor-unit firing characteristics in cerebral palsy, Dev. Med. Child Neurol., 2005, no. 47(5), p. 329.

    Google Scholar 

  21. Wakeling, J., Delaney, R., and Dudkiewicz, I., A method for quantifying dynamic muscle dysfunction in children and young adults with cerebral palsy, Gait Posture, 2007, vol. 25(4), p. 580.

    Article  PubMed  Google Scholar 

  22. Yang, J.F., Livingstone, D., Brunton, K., Kim, D., Lopetinsky, B., Roy, F., Zewdie, E., Patrick, S.K., Andersen, J., Kirton, A., Watt, J.M., Yager, J., and Gorassini, M., Training to enhance walking in children with cerebral palsy: are we missing the window of opportunity?, Semin. Pediatr. Neurol., 2013, no. 20(2), p. 106.

    Google Scholar 

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology (State University), Dolgoprudnyi, Moscow oblast, 141700, Russia

    E. S. Zhvansky & Y. S. Levik

  2. Moscow Research Institute of Pediatrics and Pediatric Surgery, Ministry of Health of the Russian Federation, Moscow, 127412, Russia

    O. N. Tsyshkova & E. S. Keshishyan

  3. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, 127994, Russia

    E. S. Zhvansky, A. A. Grishin & Y. S. Levik

  4. OOO Kosima, Moscow, Russia

    A. A. Grishin

  5. Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179, Rome, Italy

    Y. P. Ivanenko

Authors
  1. E. S. Zhvansky
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  2. O. N. Tsyshkova
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  3. A. A. Grishin
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  4. Y. P. Ivanenko
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  5. Y. S. Levik
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  6. E. S. Keshishyan
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Correspondence to E. S. Zhvansky.

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Original Russian Text © E.S. Zhvansky, O.N. Tsyshkova, A.A. Grishin, Y.P. Ivanenko, Y.S. Levik, E.S. Keshishyan, 2015, published in Fiziologiya Cheloveka, 2015, Vol. 41, No. 1, pp. 49–56.

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Zhvansky, E.S., Tsyshkova, O.N., Grishin, A.A. et al. Characteristics of EMG activity in infants with movement disorders. Hum Physiol 41, 39–46 (2015). https://doi.org/10.1134/S0362119715010156

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  • DOI: https://doi.org/10.1134/S0362119715010156

Keywords

  • CP
  • diagnostics
  • infants
  • muscle activity
  • EMG spectrum
  • wavelet analysis