Enhanced FBLMS algorithm for ECG and noise removal from semg signals

In this paper, we proposed a Dual-adapted Fast Block Least Mean Squares algorithm (DA-FBLMS) to remove electrocardiogram (ECG) and noise contaminations from surface electromyography signals (sEMG). Based on an adaptive noise cancelation (ANC) structure and artificial input signals, the ANC integrated proposed algorithm distinguishes itself by the use of an iterative method characterized by a varying number of updates for every different input block, combined with an adaptive step size guided by a QRS detector and the average error of the corresponding input block. The simulations demonstrate that the proposed DA-FBLMS algorithm presents better performances during the contaminations cancellation compared to a recursive least squares algorithm (RLS) and classic FBLMS algorithm, especially in noisy and high distortion environments.