Two Stage Step-Size Scaler Adaptive Filter Design for ECG Denoising

In recent years, various healthcare wearable devices have increased to monitor electrocardiogram (ECG) continuously, but due to everyday life activities, artifacts are present in it, which may increases the false prediction of R-peak. This work presents the two-stage active noise cancellation (ANC) step- size scaler adaptive filter to remove high and low frequencies artifacts from the ECG signal. Moreover, the convergence rate and mean square error (MSE) minimization analysis of step-size scaler adaptive filter in different simulated noise environments is presented and compared with the least mean square (LMS) adaptive filter variants. The analysis shows that the step-size scaler algorithm gives a 67.34% fast convergence rate compared to the normalized LMS (NLMS) algorithm.