Using motor unit potential characterizations to estimate neuromuscular disorder level of involvement.
Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disorder. Motor unit potential (MUP) characterizations comprised of the conditional probabilities of a MUP being detected from a muscle of each of the following categories: myopathic, normal, and neuropathic, were estimated. The sets of MUP characterizations of a set of MUPs detected in a muscle were averaged to produce a set of muscle characterization measures related to the probability of the muscle belonging to each category conditioned on the set of MUPs detected. Using simulated EMG signals, the objective of this work was to evaluate the correlation between the muscle characterization measures produced by different MUP characterization methods and the level of involvement of a disorder. The results showed a correlation of 0.9 between myopathic and neuropathic muscle characterization measures and the actual level of involvement when using a Pattern Discovery (PD) method to estimate MUP characterizations. This work suggests that MUP characterizations can be used to assist clinicians in tracking the progress of a disease process.
Pino
Lou J
LJ
University of Waterloo department of Systems Design Engineering, Canada. ljpino@engmail.uwaterloo.ca
Stashuk
Daniel W
DW
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D018482:Muscle, Skeletal / Q000503:physiopathology*; D009132:Muscles / Q000473:pathology; D009424:Nervous System Physiological Phenomena; D009468:Neuromuscular Diseases / Q000175:diagnosis / Q000503:physiopathology*; D009469:Neuromuscular Junction; D011336:Probability; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted