Baseline wander and Power line interference elimination from Cardiac signals using Error Nonlinearity LMS algorithm

Abstract

Adaptive filter is a primary method to filter electrocardiogram (ECG) or Cardiac signal, because it does not need the signal statistical characteristics. In this paper we present an adaptive filter for denoising the ECG signal based on Error Nonlinearity Least Mean Square (ENLMS) algorithm. 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 and 60 Hz power line interference. 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 ENLMS based algorithm is superior to that of the LMS based algorithm in noise reduction.