HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: 105/2/700    most recent 00170.2007v1 Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Nawab, S. H. Articles by De Luca, C. J. Search for Related Content PubMed PubMed Citation Articles by Nawab, S. H. Articles by De Luca, C. J.

INNOVATIVE METHODOLOGY

Decomposition of indwelling EMG signals

¹NeuroMuscular Research Center, ²Department of Electrical and Computer Engineering, and ³Department of Biomedical Engineering, Boston University, Boston, Massachusetts

Submitted 9 February 2007 ; accepted in final form 30 April 2008

Decomposition of indwelling electromyographic (EMG) signals is challenging in view of the complex and often unpredictable behaviors and interactions of the action potential trains of different motor units that constitute the indwelling EMG signal. These phenomena create a myriad of problem situations that a decomposition technique needs to address to attain completeness and accuracy levels required for various scientific and clinical applications. Starting with the maximum a posteriori probability classifier adapted from the original precision decomposition system (PD I) of LeFever and De Luca (25, 26), an artificial intelligence approach has been used to develop a multiclassifier system (PD II) for addressing some of the experimentally identified problem situations. On a database of indwelling EMG signals reflecting such conditions, the fully automatic PD II system is found to achieve a decomposition accuracy of 86.0% despite the fact that its results include low-amplitude action potential trains that are not decomposable at all via systems such as PD I. Accuracy was established by comparing the decompositions of indwelling EMG signals obtained from two sensors. At the end of the automatic PD II decomposition procedure, the accuracy may be enhanced to nearly 100% via an interactive editor, a particularly significant fact for the previously indecomposable trains.

signal decomposition; electromyographic signal; superposition of action potentials; detection of recruitment; force-varying contractions

This article has been cited by other articles:

				P. Contessa, A. Adam, and C. J. De Luca Motor unit control and force fluctuation during fatigue J Appl Physiol, July 1, 2009; 107(1): 235 - 243. [Abstract] [Full Text] [PDF]

				R. Merletti and D. Farina Analysis of intramuscular electromyogram signals Phil Trans R Soc A, January 28, 2009; 367(1887): 357 - 368. [Abstract] [Full Text] [PDF]