In recent years, various healthcare wearable devices have increased to monitor ECG continuously, but due to everyday life activities, artifacts are present in it, which may cause false alarms. This work presents the two-stage 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 adaptive filter in different simulated noise environments is presented and compared with the LMS adaptive filter variants. The analysis shows that the step-size scaler algorithm gives a 67.34% fast convergence rate compared to the NLMS algorithm.