Validating motor unit firing patterns extracted by EMG signal decomposition.
Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two supervised classifiers that can be used to validate extracted MU firing patterns are proposed. The first classifier, the single/merged classifier (SMC), determines whether a motor unit potential train (MUPT) represents the firings of a single MU or the merged activity of more than one MU. The second classifier, the single/contaminated classifier (SCC), determines whether the estimated number of false-classification errors in a MUPT is acceptable or not. Each classifier was trained using simulated data and tested using simulated and real data. The accuracy of the SMC in categorizing a train correctly is 99% and 96% for simulated and real data, respectively. The accuracy of the SCC is 84% and 81% for simulated and real data, respectively. The composition of these classifiers, their objectives, how they were trained, and the evaluation of their performances using both simulated and real data are presented in detail.
Systems Design Engineering Department, University of Waterloo, Waterloo, Canada. hparsaei@engmail.uwaterloo.ca
Hossein
H
Parsaei
Faezeh Jahanmiri
FJ
Nezhad
Daniel W
DW
Stashuk
Andrew
A
Hamilton-Wright
D000465:Algorithms; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted