Resolving superimposed motor unit action potentials.
A new algorithm to resolve superimposed motor unit action potentials (MUAPs) is described, which uses a reduced search space and is based on the peel off approach. Knowledge specific to the problem domain, such as temporal relationships between and within motor unit action potential trains and MUAP energy information, is used to reduce the search space of motor units, possibly contributing to a superposition. The algorithm is tested using real electromyographic signals, and it demonstrates robust performance across the signals tested. For the signals tested, the average total resolution rate is 94%, the average correct resolution rate is 99.2% and the average error rate is 0.85%.
Etawil
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Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.
Stashuk
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D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D004576:Electromyography; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted