An Efficient Variable Step Size Least Mean Square Adaptive Algorithm Used to Enhance the Quality of Electrocardiogram Signal

Abstract

The main aim of this paper is to present an efficient method to cancel the noise in the ECG signal, due to various sources, by applying adaptive filtering techniques. The adaptive filter essentially reduces 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. The Least Mean Square (LMS) algorithm is familiar and simple to use for cancellation of noises. However, the low convergence rate and low signal to noise ratio are the limitations for this LMS algorithm. To enhance the performance of LMS algorithm, in this paper, we present an efficient variable step size LMS algorithms which will provide fast convergence at early stages and less misadjustment in later stages. Different kinds of variable step size algorithms are used to eliminate artifacts in ECG by considering the noises such as power line interference and baseline wander. The simulation results shows that the performance of the variable step size LMS algorithm is superior to the conventional LMS algorithm, while for sign based, the sign regressor variable step size LMS algorithm is equally efficient as that of variable step size LMS with additional advantage of less computational complexity.