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Effects of detection system parameters on cross-correlations between MUAPs generated from parallel and inclined muscle fibres

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of this study was to investigate the effects of inter-electrode distance (IED), electrode radius (ER) and electrodes configurations on cross-correlation coefficient (CC) between motor unit action potentials (MUAPs) generated in a motor unit (MU) of parallel fibres and in a MU of inclined fibres with respect to the detection system. The fibres inclination angle (FIA) varied from 0° to 180° by a step of 5°. Six spatial filters (the longitudinal single differential (LSD), longitudinal double differential (LDD), bi-transversal double differential (BiTDD), normal double differential (NDD), an inverse binomial filter of order two (IB2) and maximum kurtosis filter (MKF)), three values of IED and three values of ER were considered. A cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers was used to simulate the MUAPs. The cross-correlation coefficient analysis showed that with the increase of the FIA, the pairs of MUAPs detected by the IB2 system were more correlated than those detected by the five other systems. For each FIA, the findings also showed that the MUAPs pairs detected by BiTDD, NDD, IB2 and MKF systems were more correlated with smaller IEDs than with larger ones, while inverse results were found with the LSD and LDD systems. In addition, the pairs of MUAPs detected by the LDD, BiTDD, IB2 and MKF systems were more correlated with large ERs than with smaller ones. However, inverse results were found with the LSD and NDD systems.
Słowa kluczowe
Rocznik
Strony
87--97
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Université de Boumerdes, Faculty of Technology, Department Engineering of Electrical Systems, 35000 Boumerdes, Algeria
  • Université de Sétif 1, Faculty of Technology, Department of Electronics, LIS Laboratory,19000 Sétif, Algeria
  • Université de Sétif 1, Faculty of Technology, Department of Electronics, LIS Laboratory,19000 Sétif, Algeria
  • Université de Boumerdes, Faculty of Technology, Department Engineering of Electrical Systems, 35000 Boumerdes, Algeria
Bibliografia
  • 1. Dimitrova NA, Dimitrov AG, Dimitrov GV, et al. Calculation of extracellular potentials produced by an inclined muscle fibre at a rectangular plate electrode. Med Eng Phys. 1999:21(8):583-588. https://doi.org/10.1016/s1350-4533(99)00087-9
  • 2. Mesin L, Farina D. Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation. IEEE Trans Biomed Eng. 2004:51(9):1521-1529. https://doi.org/10.1109/TBME.2004.827551
  • 3. Teklemariam A, Hodson-Tole EF, Reeves ND, Costen NP, Cooper G, et al. A finite element model approach to determine the influence of electrode design and muscle architecture on myoelectric signal properties. PLoS-ONE. 2016:11(2):1-18. https://doi.org/10.1371/journal.pone.0148275
  • 4. Farina D, Cescon C, Merletti R, et al. Influence of anatomical, physical, and detection-system parameters on surface EMG. Biol Cybern. 2002:86(6):445-456. https://doi.org/10.1007/s00422-002-0309-2
  • 5. Farina D, Merletti R, Enoka RM, et al. The extraction of neural strategies from the surface EMG. J Appl Physiol. 2004:96(4):1486-1495. https://doi.org/10.1152/japplphysiol.01070.2003
  • 6. Messaoudi N, Bekka RE. Simulated surface EMG signal as a function of physiological and non-physiological parameters: Analyze and interpretation. 2015: The Fourth International Conference on Electrical Engineering, ICEE2015, Boumerdes, Algeria, Proceedings, IEEE Xplore. https://doi.org/10.1109/INTEE.2015.7416801
  • 7. Fuglevand A, Winter DA, Patla AE, Stashuk D, et al. Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing. Biol Cybern. 1992:67(2):143-153. https://doi.org/10.1007/BF00201021
  • 8. Farina D, Arendt-Nielsen L, Merletti R, Indino B, Graven-Nielsen T, et al. Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle. IEEE Trans Biomed Eng. 2003:50(3):354-364. https://doi.org/10.1109/TBME.2003.808830
  • 9. Zhou P, Suresh NL, Lowery MM, Rymer WZ, et al. Nonlinear spatial filtering of multichannel surface electromyogram signals during low force contractions. IEEE Trans Biomed Eng. 2009:56(7):1871-1879. https://doi.org/10.1109/TBME.2009.2017736
  • 10. Östlund N, Yu J, Roeleveld K, Karlsson JS, et al. Adaptive spatial filtering of multichannel surface electromyogram signals. Med Biol Eng Comput. 2004:42(6):825-831. https://doi.org/10.1007/BF02345217
  • 11. Messaoudi N, Bekka RE, Belkacem S, et al. Cross-Correlation coefficient as a means for estimating the effect of MVC level according to the fibres inclination’, The Fifth International Conference on Electrical Engineering. 2017: ICEE2017, Boumerdes, Algeria, Proceedings, IEEE Xplore. https://doi.org/10.1109/ICEE-B.2017.8192166
  • 12. Beck TW, Housh TJ, Cramer JT, Weir JP, et al. The effects of inter-electrode distance over the innervation zone and normalization on the electromyographic amplitude and mean power frequency versus concentric, eccentric, and isometric torque relationships for the vastus lateralis muscle. J Electromyogr Kinesiol. 2009:19(2): 219-231. https://doi.org/10.1016/j.jelekin.2007.07.007
  • 13. Messaoudi N, Bekka RE. From single fibre action potential to surface electromyographic signal: A simulation study. Third International Conference, IWBBIO 2015, Granada, Spain, Proceedings, Part I, LNCS 9043, April 15-17, 2015:315–324. https://doi.org/10.1007/978-3-319-16483-0_32
  • 14. Farina D, Mesin L, Simone M, Merletti R, et al. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE Trans Biomed Eng. 2004: 1(3): 415-426. https://doi.org/10.1109/TBME.2003.820998
  • 15. Fuglevand AJ, Winter DA, Patla AE, et al. Models of recruitment and rate coding organisation in motor-unit pools. J Neurophysiol. 1993:70(6):2470-2488. https://doi.org/10.1152/jn.1993.70.6.2470
  • 16. Rosenfalck P. Intra and extracellular fields of active nerve and muscle fibres: A physico-mathematical analysis of different models. Acta Physiol Scand Suppl. 1969:321:1-168.
  • 17. Messaoudi N, Bekka RE, Ravier P, Harba R, et al. Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments. J Electromyog Kinesiol. 2017:32(1): 70-82. https://doi.org/10.1016/j.jelekin.2016.12.006
  • 18. Keenan KG, Valero-Cuevas FJ. Experimentally valid predictions of muscle force and EMG in models of motor-unit function are most sensitive to neural properties. J. Neurophysiol. 2007:98(3):1581-1590. https://doi.org/10.1152/jn.00577.2007
  • 19. Keenan KG, Farina D, Meyer FG, Merletti R, Enoka RM, et al. Sensitivity of the cross-correlation between simulated surface EMGs for two muscles to detect motor unit synchronization. J App Physiol. 2007:102:1193-1201. https://doi.org/10.1152/japplphysiol.00491.2006
  • 20. Farina D, Merletti R. A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE Trans Biomed Eng. 2001:48(6):637-646. https://doi.org/10.1109/10.923782
  • 21. Messaoudi N, Bekka RE, Belkacem S, et al. Classification of the systems used in surface electromyographic signal detection according to the degree of isotropy. Adv Biomed Eng. 2018:7(1):107-116. https://doi.org/10.14326/abe.7.107
  • 22. Barbero M, Rainoldi A, Merletti R, et al. Atlas of muscle innervation zones: understanding surface EMG and its applications. Springer, Italy 2012. https://doi.org/10.1007/978-88-470-2463-2
  • 23. Merletti R, Farina D. (edts) Surface Electromyography: physiology, engineering and applications, IEEE Press / J Wiley, USA, May 2016.
  • 24. Afsharipour B, Soedirdjo S, Merletti R, et al. Two-dimensional surface EMG: The effects of electrode size, interelectrode distance and image truncation. Biomed Sig Process Control. 2019: 49(1):298-307. https://doi.org/10.1016/j.bspc.2018.12.001
  • 25. Messaoudi N, Bekka RE, Belkacem S, et al. Influence of fibers inclination on the degree of gaussianity of simulated surface EMG signals. ICBBT 2020, May 22–24, 2020, Xi'an, China. https://doi.org/ 10.1145/3405758.3405781
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75645625-220e-49db-843f-375a8581fbf6
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