Use of entropy, spectral parameters and recurrence quantification analysis for the evaluation of muscle fatigue from surface electromyography

Abstract

Surface electromyography (EMG) is commonly used to detect muscle activation, to find muscle activation level compared to a reference-especially at low force levels- and to evaluate muscle fatigue level. EMG signal is nonstationary and is the response of complex system. The most commonly used method to assess muscle fatigue is to compute decline in mean (MNF) and median (MDF) frequencies which are found from power spectrum of EMG signal. As the muscle continuously passes to fatigue state during a prolonged muscle activation, conduction velocity declines whereas motor unit synchronization increases. The motor unit synchronization should effect EMG signal complexity. Considering this, the EMG signals are analysed with entropy and recurrence quantification analysis (RQA) methods along with MNF and MCF parameters since entropy and RQA methods give measure to determine complexity changes in the signal. It was found that MNF decline is accompanied with entropy decline and %DET increase as the fatigue occurs. These findings could be explained by increased regularity caused by motor unit synchronization.