Reliability of muscle fibre conduction velocity distribution estimation from surface EMG

Most of the neuromuscular diseases induce changes in muscle fibre characteristics. For example, Duchenne dystrophy is characterized by a specific loss of fast fibres, and an increase in small diameter fibres. These morphological changes may lead to large modifications in the distribution of fibre diameters, possibly producing bimodal distributions. It has already been shown that it is possible to reveal these morphological modifications through the distribution of muscle fibre conduction velocity (MFCV) as estimated from needle electromyography (EMG) recordings. In this paper, we investigate whether such changes can be extracted from surface EMG signals. Simulation allows generation of surface EMG signals in which features are well described especially at a morphological level. Therefore, we generated a database of simulated signals both in voluntary and electrically elicited contraction conditions using a bimodal distribution of muscle fibre diameters. MFCV distributions were computed using two short-term methods based on cross-correlation and peak-to-peak techniques for voluntary contraction signals, and using a deconvolution method in time domain for electrically elicited signals. MFCV distributions were compared with true ones, as generated from modelling. This work reveals that estimating MFCV distribution through these methods does not appear yet as precise and robust enough to accurately characterize changes in redistribution of various muscle fibre diameters. However, it opens to new experimental protocols that can be explored in order to improve the robustness of MFCV distribution estimation for the follow-up of patients suffering from neuromuscular disorders.