Adaptive motor unit action potential clustering using shape and temporal information.
An adaptive algorithm is described that groups motor unit action potentials (MUAPs), detected in a composite EMG signal during signal decomposition, and creates partial motor unit action potential trains (MUAPTs). Data-driven MUAP shape and motor unit firing-pattern based criteria are used to form the clusters. An algorithm for estimating MUAPT temporal parameters, which provides accurate estimates even for partially defined trains, is used to obtain firing-pattern information. No a priori knowledge is required regarding the number of clusters or the distribution of their template shapes. The clustering algorithm when applied to real concentric-needle detected MUAP data provides accurate and useful clustering results. Compared to a classical leader-based algorithm, it provides more robust performance, is better able to estimate the true number of motor units represented in a set of detected MUAPs, and obtains more complete and accurate MUAPTs.
Stashuk
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Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.
Qu
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Y
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D004576:Electromyography; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors