Advertisement

Single motor unit analysis from spatially filtered surface electromyogram signals. Part I: Spatial selectivity

  • D. Farina
  • E. Schulte
  • R. Merletti
  • G. Rau
  • C. Disselhorst-Klug
Article

Abstract

The aim of the study was to compare experimentally, on the basis of single motor unit (MU) activities, the selectivity of different spatial filters commonly used to detect surface electromyogram (EMG) signals. Surface EMG signals were recorded from the biceps brachii and the upper trapezius muscle of five subjects using a two-dimensional (2D) electrode array consisting of 16 pin electrodes. The subjects performed isometric contractions at different elbow angles and shoulder abduction and flexion. The same monopolar surface EMG signals were filtered using longitudinal single and double differential, transverse single and double differential and normal double differential filters. From the single MU action potentials, extracted by automatic EMG decomposition, indexes of transverse (perpendicular with respect to the fibre direction) and longitudinal (along the fibre direction) selectivity were computed. The number of detected MUs was 48 for the upper trapezius, with the arms held in the sagittal plane, and 52 when the arms were held in the frontal plane; 85 MUs were identified from the biceps brachii contractions. The results showed that transverse selectivity was significantly higher for the 2D and transverse onedimensional (1D) filters with respect to the 1D longitudinal filters, whereas longitudinal selectivity was higher (i.e. MU action potentials were shorter) for the 2D filter and the longitudinal double differential filter. In particular, the relative attenuation of potential amplitude moving 5 mm from the source was, on average (for the two muscles), 16.5% for the least selective filter in the transverse direction (longitudinal single differential) and 35.7% for the most selective one in the same direction (transverse double differential). The MU action potential duration was, on average, 13.8 ms for the most selective filter in the longitudinal direction (longitudinal double differential) and 18.7 ms for the least selective one (transverse double differential). The normal double differential filter resulted in spatial selectivity indexes that were not statistically different in the two directions from those of the best filters in each direction.

Keywords

Surface electromyography Motor unit action potentials Spatial filtering Surface EMG decomposition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Disselhorst-Klug, C., Silny, J., andRau, G. (1997): ‘Improvement of spatial resolution in surface-EMG: a theoretical and experimental comparison of different spatial filters’,IEEE Trans. Biomed. Eng.,44, pp. 567–574CrossRefGoogle Scholar
  2. Disselhorst-Klug, C., Silny, J., andRau, G. (1998): ‘Estimation of the relationship between noninvasively detected activity of single motor units and their characteristic pathological changes by modelling’,J. Electromyogr. Kinesiol.,8, pp. 323–335CrossRefGoogle Scholar
  3. Disselhorst-Klug, C., Rau, G., Schmeer, A., andSilny, J. (1999): ‘Non-invasive detection of the single motor unit action potential by averaging the spatial potential distribution triggered on a spatially filtered motor unit action potential’,J. Electromyogr. Kinesiol.,9, pp. 67–72CrossRefGoogle Scholar
  4. Disselhorst-Klug, C., Bahm, J., Ramaekers, V., Trachterna, A., andRau, G. (2000): ‘Non-invasive approach of motor unit recording during muscle contractions in humans’,Eur. J. Appl. Physiol.,83, pp. 144–150CrossRefGoogle Scholar
  5. Dumitru, D., andKing, J. C. (1991): ‘Far-field potentials in muscle’,Muscle & Nerve,14, pp. 981–989Google Scholar
  6. Farina, D., andRainoldi, A. (1999): ‘Compensation of the effect of sub-cutaneous tissue layers on surface EMG: a simulation study’,Med. Eng. Phys.,21, pp. 487–496Google Scholar
  7. Farina, D., andCescon, C. (2001): ‘Concentric ring electrode systems for non-invasive detection of single motor unit activity’,IEEE Trans. Biomed. Eng.,48, pp. 1326–1334Google Scholar
  8. Farina, D., Cescon, C., andMerletti, R. (2002a): ‘Influence of anatomical, physical and detection system parameters on surface EMG’,Biol. Cybern.,86, pp. 445–456CrossRefGoogle Scholar
  9. Farina, D., Fosci, M., andMerletti, R. (2002b): ‘Motor unit recruitment strategies investigated by surface EMG variables’,J. Appl. Physiol.,92, pp. 235–247Google Scholar
  10. Farina, D., Madeleine, P., Graven-Nielsen, T., Merletti, R., andArendt-Nielsen, L. (2002c): ‘Standardising surface electromyogram recordings for assessment of activity and fatigue in the human upper trapezius muscle’,Eur. J. Appl. Physiol.,86, pp. 469–478CrossRefGoogle Scholar
  11. Farina, D., Merletti, R., Indino, B., Nazzaro, M., andPozzo, M., (2002d): ‘Cross-talk between knee extensor muscles. Experimental and model results’,Muscle & Nerve,26, pp. 681–695CrossRefGoogle Scholar
  12. Gydikov, A., Kosarov, D., andDimitrov, G. V. (1979): ‘Length of the summated depolarized area and duration of the depolarizing and repolarizing processes in the motor unit under different conditions’,Electromyogr. Clin. Neurophysiol.,19, pp. 229–248Google Scholar
  13. Hermens, H. J., andFreriks, B. (1997): ‘The state of the art on sensors and sensor placement procedures for surface electromyography: a proposal for sensor placement procedures’. SENIAM Project Report, Roessingh Research & Development (Pub.)Google Scholar
  14. Hogrel, J. Y., andDuchêne, J. (1999): ‘A sEMG-based system for clinical applications using laplacian electrodes’. Proc. of the 4th General SENIAM Workshop, The Netherlands, pp. 172–177Google Scholar
  15. Huppertz, H. J., Disselhorst-Klug, C., Silny, J., Rau, G., andHeimann, G. (1997): ‘Diagnostic yield of noninvasive high-spatial-resolution-EMG in neuromuscular disease’,Muscle & Nerve,20, pp. 1360–1370CrossRefGoogle Scholar
  16. Jensen, C., Vasseljen, O., andWestgaard, R. H. (1993): ‘The influence of electrode position on bipolar surface electromyogram recordings of the upper trapezius muscle’,Eur. J. Appl. Physiol.,67, pp. 266–273Google Scholar
  17. Masuda, T., Miyano, H., andSadoyama, T. (1985): ‘A surface electrode array for detecting action potential trains of single motor units’,Electroenceph. Clin. Neurophysiol.,60, pp. 435–443CrossRefGoogle Scholar
  18. Merletti, R., Farina, D., andGranata, A. (1999): ‘Non-invasive assessment of motor unit properties with linear electrode arrays’, in ‘clinical neurophysiology: from receptors to perception’ (Elsevier Publisher, 1999), pp. 293–300Google Scholar
  19. Ramaekers, V., Disselhorst-Klug, C., Schmeider, J., Silny, J., Forst, J., Forst, R., Kotlarek, F., andRau, G. (1993): ‘Clinical application of a noninvasive multi-electrode array EMG for the recording of single motor unit activity’,Neuropaediatrics,24, pp. 134–138Google Scholar
  20. Rau, G., Disselhorst-Klug, C., andSilny, J. (1977): ‘Noninvasive approach to motor unit characterization: muscle structure, membrane dynamics and neuronal control’,J. Biomech.,30, pp. 441–446Google Scholar
  21. Rau, G., andDisselhorst-Klug, C.,(1997): ‘Principles of highspatial-resolution surface EMG (HSR-EMG): single motor unit detection and application in the diagnosis of neuromuscolar disorders’,J. Electromyogr. Kinesiol.,7, pp. 233–239CrossRefGoogle Scholar
  22. Reucher, H., Rau, G., andSilny, J. (1987a): ‘Spatial filtering of noninvasive multielectrode EMG: part I—Introduction to measuring technique and applications’,IEEE Trans. Biomed. Eng.,34, pp. 98–105Google Scholar
  23. Reucher, H., Rau, G., andSilny, J. (1987b): ‘Spatial filtering of noninvasive multielectrode EMG: part II—Filter performance in theory and modelling’,IEEE Trans. Biomed. Eng.,34, pp. 106–113Google Scholar
  24. Roeleveld, K., Stegeman, D. F., Vingerhoets, H. M., andvan Oosterom, A. (1997): ‘The motor unit potential distribution over the skin surface and its use in estimating the motor unit location’,Acta Physiol. Scand.,161, pp. 465–472CrossRefGoogle Scholar
  25. Schulte, E., Farina, D., Disselhorst-Klug, C., Merletti, R., andRau, G. (2002): ‘Non-invasive estimation of motor unit conduction velocity during isometric and dynamic contractions’. Proc. XIV Congress of International Society of Electrophysiology and Kinesiology, pp. 171–172Google Scholar
  26. Schulte, E., Farina, D., Rau, G., Merletti, R., andDisselhorst-Klug, C. (2003): ‘Single motor unit analysis from spatially filtered surface electromyogram signals. Part 2: Conduction velocity estimation’,Med. Biol. Eng. Comput.,41, pp. 338–345Google Scholar
  27. Wee, A. S., andAshley, R. A. (1990): ‘Volume-conducted or “far-field” compound action potentials originating from the intrinsic-hand muscles’,Electromyogr. Clin. Neurophysiol.,30, pp. 325–333Google Scholar

Copyright information

© IFMBE 2003

Authors and Affiliations

  • D. Farina
    • 1
  • E. Schulte
    • 2
  • R. Merletti
    • 1
  • G. Rau
    • 2
  • C. Disselhorst-Klug
    • 2
  1. 1.Centro di Bioingegneria, Dipartimento di ElettronicaPolitecnico di TorinoTorinoItaly
  2. 2.Institute for Biomedical TechnologiesHelmholtz InstituteAachenGermany

Personalised recommendations