Estimation of muscle fiber conduction velocity from two-dimensional surface EMG recordings in dynamic tasks

Abstract

The aims of this study are (1) to demonstrate that multi-channel surface electromyographic (EMG) signals can be detected with negligible artifacts during fast dynamic movements with an adhesive two-dimensional (2D) grid of 64 electrodes and (2) to propose a new method for the estimation of muscle fiber conduction velocity from short epochs of 2D EMG recordings during dynamic tasks. Surface EMG signals were collected from the biceps brachii muscle of four subjects with a grid of 13 × 5 electrodes during horizontal elbow flexion/extension movements (range 120–170°) at the maximum speed, repeated cyclically for 2 min. Action potentials propagating between the innervation zone and tendon regions could be detected during the dynamic task. A maximum likelihood method for conduction velocity estimation from the 2D grid using short time intervals was developed and applied to the experimental signals. The accuracy of conduction velocity estimation, assessed from the standard deviation of the residual of the regression line with respect to time, decreased from (range) 0.20–0.33 m/s using one column to 0.02–0.15 m/s when combining five columns of the electrode grid. This novel method for estimation of muscle fiber conduction velocity from 2D EMG recordings provides an estimate which is global in space and local in time, thus representative of the entire muscle yet able to track fast changes over the execution of a task, as is required for assessing muscle properties during fast movements.