Resolving superimposed MUAPs using particle swarm optimization.
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%.
Marateb
Hamid Reza
HR
Laboratorio di Ingegneria del Sistema Neuromuscolare, Dipartimento di Elettronica, Politecnico di Torino, Turin 10129, Italy. hamid.marateb@polito.it
McGill
Kevin C
KC
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