Adaptive noise removal in the ECG using the Block LMS algorithm

The electrocardiogram (ECG) is the most commonly used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG signals are corrupted by artifacts. So the noise removal is a classical problem in ECG records, that generally produces artifactual data when measuring the ECG parameters. The Block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. In this paper, we present a BLMS algorithm for removing artifacts preserving the low frequency components and tiny features of the ECG. Finally, we have applied this algorithm on ECG signals from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the BLMS algorithm is superior than the LMS algorithm.