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Tytuł artykułu

Evaluation of the training objectives with surface electromyography

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this work, the multifractal analysis of the kinesiological surface electromyographic signal is proposed. The goal was to investigate the level of neuromuscular activation during complex movements on the laparoscopic trainer. The basic issue of this work concerns the changes observed in the signal obtained from the complete beginner in the field of using laparoscopic tools and the same person subjected to the series of training. To quantify the complexity of the kinesiological surface electromyography, the nonlinear analysis technique, namely, the multifractal detrended fluctuation analysis, was adopted. The analysis was based on the parameters describing the multifractal spectrum – the Hurst exponent – and the spectrum width. The statistically significant differences for a selected group of muscles at the different states (before and after training) are presented. In addition, as the base case, the relaxation state was considered and compared with the working states.
Słowa kluczowe
Rocznik
Strony
25--32
Opis fizyczny
Bibliogr. 21 poz., rys., wykr.
Twórcy
autor
  • Institute of Physics, University of Silesia, Katowice, Poland
  • Department of Medical Education, Faculty of Medicine, Jagiellonian University Medical College, Krakow, Poland
autor
  • Institute of Physics, University of Silesia, Katowice, Poland; and Silesian Center for Education and Interdisciplinary Research, University of Silesia, Chorzow, Poland
Bibliografia
  • 1. Aggarwal R, Moorthy K, Darzi A. Laparoscopic skills training and assessment. Br J Surg 2004;91:1549–58.
  • 2. Forsman M, Birch L, Zhang Q, Kadefors R. Motor unit recruitment in the trapezius muscle with special reference to coarse arm movements. J Electromyogr Kinesiol 2001;11:207–16.
  • 3. Hug F. Can muscle coordination be precisely studied by surface electromyography? J Electromyogr Kinesiol 2011;21:1–12.
  • 4. Merletti R, Parker PA. Electromyography: physiology, engineering, and noninvasive applications, volume 11. Hoboken, New Jersey: John Wiley & Sons, 2004.
  • 5. Barbero M, Merletti R, Rainoldi A. Atlas of muscle innervation zones: understanding surface electromyography and its applications. Springer Science & Business Media, 2012.
  • 6. Gierałtowski J, Żebrowski JJ, Baranowski R. Multiscale multifractal analysis of heart rate variability recordings with a large number of occurrences of arrhythmia. Phys Rev E Stat Nonlin Soft Matter Phys 2012;85:021915.
  • 7. Goldberger AL. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 1996;347: 1312–4.
  • 8. Hampson KM, Mallen EA. Multifractal nature of ocular aberration dynamics of the human eye. Biomed Opt Express 2011;2: 464–70.
  • 9. Bryce RM, Sprague KB. Revisiting detrended fluctuation analysis. Sci Rep 2012;2:315.
  • 10. Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG. Surface electromyography signal processing and classification techniques. Sensors 2013;13:12431–66.
  • 11. Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Signal Process Control 2015;18: 334–59.
  • 12. Goldberger AL, Rigney DR, West BJ. Chaos and fractals in human physiology. Sci Am 1990;262:42–9.
  • 13. Makowiec D, Gałąska R, Dudkowska A, Rynkiewicz A, Zwierz M. Long-range dependencies in heart rate signal – revisited. Physica A 2006;369:632–44.
  • 14. Peter K. The ABC of EMG – a practical introduction to kinesiological electromyography. USA: Noraxon Inc, 2005.
  • 15. Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 2002;316:87–114.
  • 16. Kantelhardt JW. Fractal and multifractal time series. In: Encyclopedia of complexity and systems science. New York: Springer-Verlag, 2009:3754–79.
  • 17. Gupta V, Suryanarayanan S, Reddy NP. Fractal analysis of surface EMG signals from the biceps. Int J Med Inform 1997;45:185–92.
  • 18. Ivanov PC, Amaral LA, Goldberger AL, Havlin S, Rosenblum MG, Struzik ZR, et al. Multifractality in human heartbeat dynamics. Nature 1999;399:461–5.
  • 19. Ihlen EA. Introduction to multifractal detrended fluctuation analysis in Matlab. Front Physiol 2012;3:141.
  • 20. Lakhtakia A, Messier R, Varadan VV, Varadan VK. Self-similarity versus self-affinity: the Sierpinski gasket revisited. J Phys A Math Gen 1986;19:L985.
  • 21. Abry P, Goncalves P, Levy Vehel J. Scaling, fractals and wavelets. Digital signal and image processing series. London, UK: ISTE–John Wiley & Sons, Inc., 2009.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-48fb6d3f-fd7e-404d-91d6-6dd60b52fb6b
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