High-yield decomposition of surface EMG signals

Nawab, S. Hamid; Chang, Shey-Sheen; De Luca, Carlo J.

doi:10.1016/j.clinph.2009.11.092

« Previous Next »Clinical Neurophysiology
Volume 121, Issue 10 , Pages 1602-1615, October 2010

High-yield decomposition of surface EMG signals

S. Hamid Nawab
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- NeuroMuscular Research Center, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
Shey-Sheen Chang
- Delsys Inc., Boston, MA 02215, USA
Carlo J. De Luca
- Delsys Inc., Boston, MA 02215, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
- NeuroMuscular Research Center, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Neurology, Boston University, Boston, MA 02215, USA
- Corresponding author at: Delsys, Inc., 650 Beacon Street, 6th Floor, Boston MA 02215, USA. Tel.: +1 617 353 9756.

Accepted 19 November 2009.

Abstract

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.

Keywords: Decomposition, Surface EMG, Motor units, Action potentials, Firings, Firing rate