Citation

Farah G, Hewson DJ, Duchene J. J. Electromyogr. Kinesiol. 2006; 16(6): 669-676.

Affiliation

Institut Charles Delaunay, FRE CNRS 2848, Université de Technologie de Troyes, 12, rue Marie Curie, BP 2060, F-10010 Troyes cedex, France.

Copyright

(Copyright © 2006, Elsevier Publishing)

DOI

10.1016/j.jelekin.2005.11.010

PMID

16458024

Abstract

The aim of this paper is to develop a method to extract relevant activities from surface electromyography (SEMG) recordings under difficult experimental conditions with a poor signal to noise ratio. High amplitude artifacts, the QRS complex, low frequency noise and white noise significantly alter EMG characteristics. The CEM algorithm proved to be useful for segmentation of SEMG signals into high amplitude artifacts (HAA), phasic activity (PA) and background postural activity (BA) classes. This segmentation was performed on signal energy, with classes belonging to a chi(2) distribution. Ninety-five percent of HAA events and 96.25% of BA events were detected, and the remaining noise was then identified using AR modeling, a classification based upon the position of the coordinates of the pole of highest module. This method eliminated 91.5% of noise and misclassified only 3.3% of EMG events when applied to SEMG recorded on passengers subjected to lateral accelerations.

Language: en