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.