Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy. (May 2021) - Wang, Shurun; Tang, Hao; Wang, Bin; Mo, Jia
Highlights: The sEMG signals of the bicep brachii were collected, and the physiological signals were preprocessed. We propose a rapid realized algorithm of refined composite multiscale sample entropy (R2CMSE). Compared with RCMSE, the calculation speed of R2CMSE is very fast. MSE, CMSE and R2CMSE are used to analysis the complexity of white noise and 1/f noise. MAE, MFE, MPE, MSE and R2CMSE are used to distinguish muscle non-fatigue and fatigue conditions. Abstract: The analysis of human muscle fatigue is of great significance to human physiological activities. Surface electromyography (sEMG) is the widely used technique to analyze muscle fatigue due to its non-invasiveness. However, sEMG signals are complex, non-linear, and multiple time scales. Hence, multiscale entropy is an effective method to quantify the process of muscle fatigue. In this study, we proposed rapid refined composite multiscale sample entropy (R2CMSE) and used it to analyze the process of muscle fatigue. Firstly, R2CMSE was utilized to characterize the complexity and validity of noise signals on different scales and lengths. Secondly, isometric contraction activities of the biceps brachii were recorded by using sEMG from ten subjects. Then, combined with the sEMG signals of all subjects, a three-dimensional map of scale-length-entropy was constructed to determine an appropriate time scale and data length. Meanwhile, a two-way repeated-measures ANOVA was operated, and the results showed that non-fatigue and fatigue conditions exist significant differences under all algorithms. Finally, R2CMSE was used to quantify the fatigue process to analyze the reliability of its application to different subjects. It was shown that compared with other multiscale entropy algorithms, R2CMSE was faster in the calculation, less dependent on different data lengths, and more robust at different time scales. The proposed algorithm can also extract the hidden information of sEMG signals and investigate the process of muscle fatigue more effectively.