title: A genetic algorithm for the resolution of superimposed motor unit action potentials.
abstract: This paper presents a novel method, which aims at resolving difficult superimpositions of motor unit action potentials (MUAPs) obtained from single-channel intramuscular electromyographic recordings. Resolution is achieved by means of a genetic algorithm (GA) combined with a gradient descent method. This dual optimization scheme has been tested by means of simulations of isolated superimpositions involving two to six MUAPs, along with simulated extended signals of 10-s duration where the density reached 300 MUAPs/s. Of the hundreds of isolated superimpositions tested, more than 90% of the MUAPs were positively identified. With extended signals, identification rates of better than 85% were obtained. The GA alone accounted for up to an 8% improvement over the decomposition conducted using only template matching.
authors: Département de Physiologie, Institut de Génie Biomédical, Université de Montréal, Montréal QC H3T 1J4 Canada.
authors: Joël R
authors: JR
authors: Florestal
authors: Pierre A
authors: PA
authors: Mathieu
authors: Réjean
authors: R
authors: Plamondon
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*; D009469:Neuromuscular Junction / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D009435:Synaptic Transmission / Q000502:physiology*