Filtering Electrocardiographic Signals using filtered- X LMS algorithm

In this paper, a simple and efficient filteredX Least Mean Square (FXLMS) algorithm is used for the removal of different kinds of noises from the ECG signal. The adaptive filter essentially minimizes 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. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle artifacts and motion artifacts. Finally different adaptive structures are implemented to remove artifacts from ECG signals and tested on real signals obtained from MITBIH data base. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio.