De-noising surface electromyograms using an adaptive wavelet approach

The recorded surface Electromyogram (sEMG) signals is inevitably influenced by noise during the signal acquisition process. Because of the constant deviation that is known as the Pseudo-Gibbs phenomenon, which exists in conventional wavelet threshold de-noising, noise pertaining to sEMG is inevitable. In order to avoid or minimize noise in the recorded sEMG signals, the objective of this study is to investigate the feasibility of developing a new de-nosing function that uses the advantage of the adaptive threshold adopted in the Brige-Massart algorithm and test the performance of the new function. The simulation results show that applying this new de-noising function could effectively remove noise in the recorded sEMG signals. Furthermore, when compared with conventional threshold de-noising function, the new function achieved higher performed de-noising effect, and thereby enabling better signal analysis subsequently.