Adaptive block SSA based ANC implementation for high performances ECG removal from sEMG signals

This study addresses electrocardiogram (ECG) pulses removal from surface electromyography (sEMG) recordings. We describe a block singular spectrum analysis based adaptive noise canceler (BSSA-ANC) in order to enhance the filtering performance of sEMG signals. The proposed method distinguishes itself by adapting the eigenvalues of every input block data, using an adaptive noise canceling (ANC) filter based on error gradient minimization, during the grouping stage of the well-known SSA technique. Using semi-artificially prepared signals, we demonstrate that the least mean square (LMS) when combined with the proposed technique provides higher filtering performances than with standard mean. The simulation results using real world data confirm the improved noise rejection obtained with the proposed method.