Removing artifacts from electrocardiographic signals using independent components analysis

In this work, we deal with the elimination of artifacts (electrodes, muscle , respiration, etc.) from the electrocardiographic (ECG) signal. We use a n ew tool called independent component analysis (ICA) that blindly separates mixed statistically independent signals. ICA can separate the signal from t he interference, even if both overlap in frequency. In order to estimate th e mixing parameters in real time, we propose a self-adaptive step-size, der ived from the study of the averaged behavior of those parameters, and a! tw o-layers neural network. Simulations were carried out to show the performan ce of the algorithm using a standard ECG database. (C) 1998 Elsevier Scienc e B.V. All rights reserved.