Published April 8, 2022 | Version v1
Journal article Open

Simulation of the electrical signal of the muscles to obtain the electromiosignal spectrum

Creators

  • 1. Kharkiv National University of Radio Electronics

Description

The object of research is the process of skeletal muscle contraction under the influence of natural electrical impulses of the nervous system or under the conditions of external electrical stimulation. The subject of research is models that describe electrical processes in muscles during contraction. The work is aimed at building an analytical model of the skeletal muscle electrical signal, which makes it possible to calculate the spectral density of this signal for further analysis.

Research methods are methods of mathematical modeling, theory of random processes and signals, methods of spectral analysis, methods of mathematical analysis.

The model of the electrical signal of the muscle as the sum of random impulse signals corresponding to the signals of motor units is studied in the work. In this regard, a signal is analyzed, which, in contrast to the Gaussian process, is formed by summing a limited number of pulse signals. It is shown that the voltage distribution law of such a signal is expressed by the sum of Gaussian functions. In the course of the study, the structure of the electromyographic signal spectrum was obtained, presented as a sum of periodic pulses shifted in time relative to each other. The relationship between the statistical properties of a random phase difference and the type of signal power spectrum has been analytically established. The obtained theoretical relations make it possible to calculate the spectral density of the electromyographic signal depending on the number of motor units and various phase shifts between them, as well as depending on the chosen law of distribution of random variables. The results of a numerical experiment are presented for a different number of motor units and different ranges of time shifts in the case of a distribution of gauss of the probability density. The results obtained can be used in assessing the degree of dysfunction of skeletal muscles in various injuries (for example, in trauma, atrophy, etc.), as well as in choosing the optimal individual parameters of electrical stimulation during rehabilitation procedures or training processes for increasing muscle mass in athletes.

Files

Simulation of the electrical signal of the muscles to obtain the electromiosignal spectrum.pdf

Files (654.4 kB)

Additional details

References

  • Bernstein, V. M., Slavutsky, J. L., Farber, B. S. (1993). Myoelectric Control of the Muscle Electrostimulation. Proceedings of Myo-Electric Control Symposium. Institute of Biomedical Engineering, UNB. 79–80.
  • Gorgey, A. S., Mahoney, E., Kendall, T., Dudley, G. A. (2006). Effects of neuromuscular electrical stimulation parameters on specific tension. European Journal of Applied Physiology, 97 (6), 737–744. doi: http://doi.org/10.1007/s00421-006-0232-7
  • Datsok, O., Prasol, I., Yeroshenko, O. (2019). Construction of biotechnical system of muscular electrical stimulation. Bulletin of the National Technical University "KhPI" A Series of "Information and Modeling", 13 (1338), 165–175. doi: http://doi.org/10.20998/2411-0558.2019.13.15
  • Yeroshenko, O., Prasol, I., Datsok, O. (2021). Simulation of an electromyographic signal converter for adaptive electrical stimulation tasks. Innovative Technologies and Scientific Solutions for Industries, 1 (15), 113–119. doi: http://doi.org/10.30837/itssi.2021.15.113
  • Kositckogo, G. I. (1985). Fiziologiia cheloveka. Moscow: Meditcina, 544.
  • Azman, M. F., Azman, A. W. (2017). The Effect of Electrical Stimulation in Improving Muscle Tone (Clinical). IOP Conference Series: Materials Science and Engineering, 260, 012020. doi: http://doi.org/10.1088/1757-899x/260/1/012020
  • Himori, K., Tatebayashi, D., Kanzaki, K., Wada, M., Westerblad, H., Lanner, J. T., Yamada, T. (2017). Neuromuscular electrical stimulation prevents skeletal muscle dysfunction in adjuvant-induced arthritis rat. PLOS ONE, 12 (6), e0179925. doi: http://doi.org/10.1371/journal.pone.0179925
  • Bernstein, V. M., Farber, B. S. (1993). Involvement of Noise Immunity Systems of Myoelectric Control of Prostheses. Proceedings of Myo-Electrlc Control Symposium. Frederiction: UNB, 42–43.
  • Gobbo, M., Maffiuletti, N. A., Orizio, C., Minetto, M. A. (2014). Muscle motor point identification is essential for optimizing neuromuscular electrical stimulation use. Journal of NeuroEngineering and Rehabilitation, 11 (1). doi: http://doi.org/10.1186/1743-0003-11-17
  • Bekhet, A. H., Bochkezanian, V., Saab, I. M., Gorgey, A. S. (2019). The Effects of Electrical Stimulation Parameters in Managing Spasticity After Spinal Cord Injury. American Journal of Physical Medicine & Rehabilitation, 98 (6), 484–499. doi: http://doi.org/10.1097/phm.0000000000001064
  • Fedorchenko, V., Prasol, I., Yeroshenko, O. (2021). Information Technology For Identification Of Electric Stimulating Effects Parameters. Information Security and Information Technologies, 200–204.
  • Rangaiian, R. M.; Nemirko, A. P. (Ed.) (2007). Analiz biomeditcinskikh signalov. Prakticheskii podkhod. Moscow: FIZMATLIT, 440.
  • Shayduk, A. M., Ostanin, S. A. (2010). Modeling Electromiographic Signal by the Means of LabVIEW. Izvestiia Altaiskogo gosudarstvennogo universiteta, 1 (65), 195–201.
  • Shaiduk, A. M., Ostanin, S. A. (2011). Vliianie fazovogo sdviga impulsov dvigatelnykh edinitc na strukturu spektra elektromiosignala. Zhurnal radioelektroniki, 6, 1–9.
  • Tikhonov, V. I., Kharisov, V. N. (2004). Statisticheskii analiz i sintez radiotekhnicheskikh ustroistv i sistem. Moscow: Radio i sviaz, 608.