Abstract :
Adaptive filter is a primary method to filter ECG signal, because it does not need the signal statistical characteristics. In this paper we present a novel adaptive filter for removing the artifacts from ECG signals based on Constrained Stability Least Mean Square (CSLMS) algorithm. This algorithm is derived based on the minimization of the squared Euclidean norm of the difference weight vector under a stability constraint defined over the posteriori estimation error. 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. 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 CSLMS based algorithm is superior to that of the LMS based algorithm in noise reduction.
Noise
Now that we understand how sound is created, let’s talk about a special kind of sound as noise. For our purposes, anything but the voice signal in which we are interested is classified as noise. We are surrounded by noise everyday – in the house, at the office, while driving in the car, even on the golf course. In some places they even add noise so that you don’t notice other noises! Anyway, we are constantly exposed to noise. Most of the time, it just makes our good tape recordings a little bit harder to understand. But, sometimes we wonder if there are actually voices on the tape because the noises are so loud!
For this primer, we are going to classify noises into three categories: additive, convolutional, and distortion. The sketch below shows a typical recording scenario and some of these noises. We will discuss each of the three categories separately.