An automatic, adaptive, information-based algorithm for the extraction of the sEMG envelope.

Surface ElectroMyography (sEMG) is widely used as a non-invasive tool for the assessment of motor control strategies. However, the standardization of the methods used for the estimation of sEMG amplitude is a problem yet to be solved; in most cases, sEMG amplitude is estimated through the extraction of the envelope of the signal via different low-pass filtering procedures with fixed cut-off frequencies chosen by the experimenter. In this work, we have shown how it is not possible to find the optimal choice of the cut-off frequency without any a priori knowledge on the signal; considering this, we have proposed an updated version of an iterative adaptive algorithm already present in literature, aiming to completely automatize the sEMG amplitude estimation. We have compared our algorithm to most of the typical solutions (fixed window filters and the previous version of the adaptive algorithm) for the extraction of the sEMG envelope, showing how the proposed adaptive procedure significantly improves the quality of the estimation, with a lower fraction of variance unexplained by the extracted envelope for different simulated modulating waveforms (p < 0.005). The definition of an entropy-based convergence criterion has allowed for a complete automatization of the process. We infer that this algorithm can ensure repeatability of the estimation of the sEMG amplitude, due to its independence from the experimental choices, so allowing for a quantitative interpretation in a clinical environment.