Detection of hidden rhythms in surface EMG signals with a non-linear time-series tool

The analysis of the surface electromyographic (sEMG) signal is particularly attractive because it provides relatively easy access to those physiological processes that allow the muscle to generate force and movement. In this paper, one of the possible applications of recurrence plot strategy to the analysis of sEMG is described. Recurrence Quantification Analysis (RQA) is an efficient time-series analysis tool pertaining to the class of non-linear dynamics time-domain processing. We analysed sEMG recorded on the biceps brachii during isometric contraction both at constant (CF) and non constant force (NCF). For comparison purposes, experimental data were analysed over epochs of 1 s so that the hypothesis of sEMG stationarity could be accepted. The analysis concerned one of the most widely used frequency parameters (namely the median frequency, MDF) and one parameter (i.e., the percent determinism %DET) extracted using the non-linear technique. Our main results are: (i) the gross average evaluated for all subjects on %DET data shows a comparable variation with respect to MDF throughout the course of CF experiments; (ii) %DET seems able to detect motor unit (MU) synchronisation; (iii) during non constant force experiments, %DET is more effective than MDF in detecting sEMG changes determined by brisk transients of force output.