Multiscale feature based analysis of surface EMG signals under fatigue and non-fatigue conditions

In this work, an attempt has been made to differentiate sEMG signals under muscle fatigue and non-fatigue conditions using multiscale features. Signals are recorded from biceps brachii muscle of 50 normal adults during repetitive dynamic contractions. After prescribed preprocessing, each signal is divided into six segments out of which first and last segments are considered in this analysis. Multiscale RMS (MSRMS) and Multiscale Permutation Entropy (MSPE) are computed for each subject in the time scales ranging from 1 to 50. The median values of the MSRMS and MSPE are calculated for further analysis. The results show an increase in amplitude for sEMG signals under fatigue condition. MSRMS values are found to be significantly higher in fatigue. An approximately constant difference in MSRMS value between fatigue and non-fatigue condition is observed over the entire time scale with a negative slope. Further, the median of MSRMS values for each subject is able to distinguish fatigue and non-fatigue conditions. Similar analysis on MSPE showed significant difference between fatigue and non-fatigue cases and lower values of MSPE is observed in fatigue. It is also observed that the median value of MSRMS and MSPE are able to distinguish these conditions. t-test for MSRMS, MSPE and their median value show high statistical significance. It appears that this method of analysis can be used for clinical evaluation of muscles.