Objective: Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs).
Methods: A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-of-principle. We tested the technology on sEMG signals from five muscles contracting isometrically at force levels ranging up to 100% of their maximal level, including those that were covered with more than 1.5cm of adipose tissue. Decomposition accuracy was measured by a new method wherein a signal is first decomposed and then reconstructed and the accuracy is measured by comparison. Results were confirmed by the more established two-source method.
Results: The number of MUAPTs decomposed varied among muscles and force levels and mostly ranged from 20 to 30, and occasionally up to 40. The accuracy of all the firings of the MUAPTs was on average 92.5%, at times reaching 97%.
Conclusions: Reported technology can reliably perform high-yield decomposition of sEMG signals for isometric contractions up to maximal force levels.
Significance: The small sensor size and the high yield and accuracy of the decomposition should render this technology useful for motor control studies and clinical investigations.
Copyright 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.