title: Robust method for estimating motor unit firing-pattern statistics.
abstract: An error-filtered estimation (EFE) algorithm for estimating the mean and standard deviation of a set of time intervals between consecutive motor unit firing times (inter-pulse intervals (IPIs)) is described. As the input IPI data are filtered and only valid IPIs are used to estimate mean and standard deviation values, the EFE algorithm provides accurate estimates even when the data defining the train of motor unit firing times are only partially complete or have several erroneous firing times. The algorithm has been evaluated using both simulated and real motor unit firing time data, and has been found to provide accurate and unbiased mean and standard deviation estimates, even when up to 70% of the IPI data are incorrect.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Ontario, Canada.
authors.forename: D
authors.initials: D
authors.lastname: Stashuk
authors.forename: Y
authors.initials: Y
authors.lastname: Qu
mesh_terms: D004576:Electromyography; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D013997:Time Factors