title: Single motor unit analysis from spatially filtered surface electromyogram signals. Part 2: conduction velocity estimation.
abstract: The aim of the study was to compare experimentally conduction velocity (CV) estimates obtained with different estimation methods based on surface electromyogram (EMG) signals detected using five spatial filters. The filters investigated were the longitudinal single and double differential, transverse single and double differential, and normal double differential. The same surface EMG signals detected as described in Part 1 were used in this work. CV was estimated with four commonly used delay estimation techniques, i.e. from the distance between the peak values of two waveforms (with and without polynomial interpolation around the peak), and by the maximum likelihood estimate (MLE) based on two or more surface EMG channels. The average standard deviation of CV estimation (for all the MUs and the two muscles together) was 0.61 m s(-1) and 0.79 m s(-1) for the peak method, with and without interpolation, respectively, and 0.50 m s(-1) and 0.31 m s(-1) for the MLE method, from two and more surface EMG channels, respectively. Moreover, the mean of CV estimates varied by as much as 1 m s(-1) depending on the spatial filter used and the method adopted for CV estimation. Considering the dependence on the spatial filter only, the average (over all estimation methods) CV estimates obtained with the five spatial filters were 4.32 m s(-1) (normal double differential), 4.23 m s(-1) (longitudinal double differential), 4.61 m s(-1) (transverse double differential), 4.64 m s(-1) (transverse single differential) and 4.03 m s(-1) (longitudinal single differential). It was concluded that the comparison of single MU CV values obtained in different studies is critical if different spatial filters and processing techniques are used for their estimation. Higher estimates of CV were attributed to a smaller reduction in non-travelling signal components and thus were assumed to be positively biased.
authors.affiliation: Institute for Biomedical Technologies, Helmholtz Institute, Aachen, Germany.
authors.forename: E
authors.initials: E
authors.lastname: Schulte
authors.forename: D
authors.initials: D
authors.lastname: Farina
authors.forename: G
authors.initials: G
authors.lastname: Rau
authors.forename: R
authors.initials: R
authors.lastname: Merletti
authors.forename: C
authors.initials: C
authors.lastname: Disselhorst-Klug
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000328:Adult; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008297:Male; D009046:Motor Neurons / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D009431:Neural Conduction / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted