Wavelet thresholding technique for sEMG denoising by baseline estimation

The surface Electromyography (sEMG) signal is affected by different sources of noises: current technology is considerably robust to the interferences of the power line or cable motion artifacts, but still there are many limitations in denoising the baseline. In this paper we introduce a new technique, named Baseline Adaptive Denoising Algorithm (BADA), for denoising the sEMG signal by wavelet thresholding procedure. In particular, the thresholds are estimated using the same baseline signal with fixed and adaptive techniques. Eventually, we verify that the proposed adaptive method performs better than the standard Donoho technique and different variations, in term of noise cancellation and distortion of the signal, quantified by a new suggested indicator of the denoising quality.