Decomposition of indwelling EMG signals

• Published in: Journal of applied physiology (1985) Vol. 105; no. 2; pp. 700 - 710
• Main Authors: Nawab, S. Hamid, Wotiz, Robert P, De Luca, Carlo J
• Format: Journal Article
• Language: English
• Published: Bethesda, MD Am Physiological Soc 01.08.2008; American Physiological Society
• Subjects: Accuracy; Action Potentials - physiology; Algorithms; Artificial Intelligence; Biological and medical sciences; Cells; Data Interpretation, Statistical; Electromyography - statistics & numerical data; Fundamental and applied biological sciences. Psychology; Humans; Innovative Methodology; Knowledge Bases; Medical equipment; Muscular system; Precision; Recruitment, Neurophysiological - physiology; Reproducibility of Results; Signal Processing, Computer-Assisted; Force; signal decomposition; Electrophysiology; superposition of action potentials; Action potential; electromyographic signal; Muscle contraction; force-varying contractions; Recruitment; Vertebrata; Mammalia; detection of recruitment; Electromyography; Detection
• ISSN: 8750-7587; 1522-1601
• DOI: 10.1152/japplphysiol.00170.2007

1 NeuroMuscular Research Center, 2 Department of Electrical and Computer Engineering, and 3 Department 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 Address for reprint requests and other correspondence: S. H. Nawab, Dept. of Electrical and Computer Engineering, Boston Univ., 8 St. Mary's St., Boston, MA 02215 (e-mail: hamid{at}bu.edu )