Study of Different Adaptive Filtering Algorithms for Reduction in Baseline Wander in ECG Signal

The electrocardiogram (ECG) is the graphical representation of heart’s functionality. It is an important tool used for the diagnosis of cardiac abnormalities. ECG signals are usually weak and susceptible to external noise and interference. Adaptive filter is a good tool to reduce the influence of ambient noise/interference on the ECG signals. Adaptive filter uses Least mean squares (LMS) algorithm, as one of most popular adaptive algorithms for active noise cancellation (ANC). The goal of the paper is to show the comparison based on signal to noise ratio of different adaptive filter algorithms like LMS, Normalized LMS (NLMS), Normalized Signed Regressor LMS (NSRLMS), Recursive Least Squares (RLS), Normalized Sign-Sign LMS (NSSLMS), used for the analysis of ECG signals with noise. The filter needs two input: the signal (primary input) and an impulse correlated with the deterministic component (reference input). Several signals to noise ratio were considered. The adaptive filters essentially minimize the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Keywords—ECG signal, Noise, Adaptive filtering, LMS, NLMS, RLS, NSSLMS.