title: Resolving superimposed MUAPs using particle swarm optimization.
abstract: This paper presents an algorithm to resolve superimposed action potentials encountered during the decomposition of electromyographic signals. The algorithm uses particle swarm optimization with a variety of features including randomization, crossover, and multiple swarms. In a simulation study involving realistic superpositions of two to five motor-unit action potentials, the algorithm had an accuracy of 98%.
authors.affiliation: Laboratorio di Ingegneria del Sistema Neuromuscolare, Dipartimento di Elettronica, Politecnico di Torino, Turin 10129, Italy. hamid.marateb@polito.it
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Marateb
authors.forename: Kevin C
authors.initials: KC
authors.lastname: McGill
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted