Wavelet compression inspired implementation for high performances and low complexity ECG removal in wireless sEMG electrodes

This study addresses the removal of electrocardiogram pulses (ECG) from surface electromyography signals (sEMG) in wireless sEMG electrodes. We describe a wavelet-compression inspired filtering technique in order to minimize the computational complexity required in wireless sEMG electrodes while providing higher filtering performance than when with standard means. Using semi-artificially prepared signals, we show that the discrete wavelet transform (DWT) enables a partial separation between the sEMG and ECG signals in time domain. Then, only the highly overlapped parts are fed into an adaptive noise cancellation (ANC) structure for lower computational complexity filtering. The simulation results confirm the improved noise rejection and the gain in computational complexity obtained with the proposed method.