A Genetic Algorithm for the Resolution of Superimposed Motor Unit Action Potentials

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.