A new method for estimating the number of motor units in a muscle

A new method termed MUESA - Motor Unit Estimation based on Stochastic Activation - was developed for estimating the number of motor units in peripheral nerve/muscle systems. What distinguishes MUESA from other estimation methods is the manner in which it deals with "alternation" or probabilistic motor unit activation. Because of "alternation", incremental increases in the observed muscle potentials often can not be interpreted in terms of the successive activation of single motor units. With MUESA, we introduce a method that interprets the muscle potentials in the context of a probabilistic activation framework. In the MUESA method, the nerve is subjected to a number of constant-intensity stimulus trains, and the resultant muscle response sequences are decomposed into their constituent motor unit action potentials. In general, if a stimulus train results in the probabilistic activation of n motor units, we can expect to see up to 2 to the power of n different potentials, with each potential representing a unique combination of active and/or inactive motor units. If all 2 to the power of n potentials are indeed observed, the decomposition of the observed potential sequence into its constituent motor unit action potentials is trivial. For the majority of the cases in which the number of observed potentials is not an integer power of 2, we have developed a novel decomposition method based on the analysis of the relative firing rates of the motor units. MUESA was evaluated by examining the estimates obtained from both control and neurogenic subjects. We also examined a number of alternative estimation strategies that do not rely on sampling procedures used in all methods published to date. These alternative methods hold the promise of avoiding the inherent error associated with sampling.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat