In this work, we deal with the elimination of artifacts (electrodes, muscle, respiration, etc) from the electrocardiographic (ECG) signal. We use a new tool called independent component analysis (ICA) that blindly separates mixed statistically independent signals. ICA can separate the signal from the interference, even if both overlap in frequency. In order to estimate the mixing parameters in real time, we propose a self-adaptive step-size, derived from the study of the averaged behavior of those parameters, and a two-layers neural network. Simulations were carried out to show the performance of the algorithm using a standard ECG database.