Abstract
Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference).
Highlights
IntroductionDifferent information on muscle activity can be collected with surface electromyograms (EMGs), e.g., concerning the muscle itself (peripheral properties) or its central control
Different information on muscle activity can be collected with surface electromyograms (EMGs), e.g., concerning the muscle itself or its central control
The same simulator with cylindrical volume conductor was used for two cases, considering a muscle with 100 motor units (MU), obtained undersampling by a factor of 4 for the simulated MU action potentials (MUAPs) used for the following tests
Summary
Different information on muscle activity can be collected with surface electromyograms (EMGs), e.g., concerning the muscle itself (peripheral properties) or its central control. Discriminating among peripheral and central myoelectric manifestations is very important, for example, in the investigation of fatigue [1]. The control of motor units (MU) reflects training [2], fatigue [3], exerted force [4], and pathology [5]. Many important physiological problems can be investigated by studying MU control, as common drive [6], muscle synergies [7], intra-muscle and inter-muscles coherence [8], corticomuscular synchronization [9]. Detailed information on MU recruitment allows improving the accuracy in many applications, such as in the estimation of the muscle force [10], contraction velocity [11], and joint angle [12] and in the control of a myoelectric prosthesis [13]
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