Estimation of mean and median frequency from synthetic sEMG signals: Effects of different spectral shapes and noise on estimation methods

The detection of electrical signs of muscle fatigue passes through the choice of appropriate spectral estimation techniques for the extraction of parameters such as Mean and Median Frequency from surface myoelectric signals. Unfortunately, despite the huge number of contributions using various spectral techniques to process signals recorded in fatiguing protocols, there is no agreement on the method that works best, and to our knowledge, the performance of the estimation techniques has been evaluated only on a limited range of spectral shapes. To study the fatigue phenomenon, it is important to consider the range of variation of the spectral parameters and then the different shapes assumed by the signal spectrum. In this work, the latter characteristics have been used to simulate synthetic myoelectric signals whose Power Spectral Density has been estimated in different noise conditions by using the following techniques: a) indirect technique by the estimation of the autocorrelation function; b) direct technique by the Welch estimation method with special attention to the window functions for signal segmentation; c) Burg autoregressive approach with special attention to the model order. The main result, after assessing that Burg's approach outperforms all the other techniques for all the examined conditions, outlines that the model's order, despite ranging between 6 and 12, depends on the spectral shape and affects the estimation of the Median Frequency. This partially overwrites previous results of the literature and provides the useful recommendation to choose, among the spectral estimation techniques, the most adaptive to the dynamics of the fatigue process.