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Scientific article
English

Experimental analysis of accuracy in the identification of motor unit spike trains from high-density surface EMG

Publication date2010
Abstract

The aim of this study was to compare the decomposition results obtained from high-density surface electromyography (EMG) and concurrently recorded intramuscular EMG. Surface EMG signals were recorded with electrode grids from the tibialis anterior, biceps brachii, and abductor digiti minimi muscles of twelve healthy men during isometric contractions ranging between 5% and 20% of the maximal force. Bipolar intramuscular EMG signals were recorded with pairs of wire electrodes. Surface and intramuscular EMG were independently decomposed into motor unit spike trains. When averaged over all the contractions of the same contraction force, the percentage of discharge times of motor units identified by both decompositions varied in the ranges 84%-87% (tibialis anterior), 84%-86% (biceps brachii), and 87%-92% (abductor digiti minimi) across the force levels analyzed. This index of agreement between the two decompositions was linearly correlated with a self-consistency measure of motor unit discharge pattern that was based on coefficient of variation for the interspike interval (R(2) = 0.68 for tibialis anterior, R(2) = 0.56 for biceps brachii, and R(2) = 0.38 for abductor digiti minimi). These results constitute an important contribution to the validation of the noninvasive approach for the investigation of motor unit behavior in isometric low-force tasks.

Keywords
  • Action Potentials/physiology
  • Adult
  • Algorithms
  • Electrodes
  • Electromyography/*methods
  • Electrophysiology
  • Humans
  • Isometric Contraction/physiology
  • Male
  • Motor Neurons/*physiology
  • Muscle Contraction/physiology
  • Muscle Fibers, Skeletal/*physiology
  • Muscle, Skeletal/physiology
  • Reproducibility of Results
Citation (ISO format)
HOLOBAR, Ales et al. Experimental analysis of accuracy in the identification of motor unit spike trains from high-density surface EMG. In: IEEE transactions on neural systems and rehabilitation engineering, 2010, vol. 18, n° 3, p. 221–229. doi: 10.1109/TNSRE.2010.2041593
Identifiers
ISSN of the journal1534-4320
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