Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays.
This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.
Farina
D
D
Department of Electronics, Politecnico di Torino, Italy.
Muhammad
W
W
Fortunato
E
E
Meste
O
O
Merletti
R
R
Rix
H
H
D000200:Action Potentials / Q000502:physiology; D004576:Electromyography; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D018485:Muscle Fibers, Skeletal / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted