Adaptive blind elimination of artifacts in ECG signals
Résumé
abstract- In this work, we deal with the elimination of artifacts (electrodes, muscle, respiration, ete-) from the electrocardiographic (ECG) signal. We use a new tool called independent component analysis (ICA) that blindly separate mixed statistical independent signals. ICA can separate the interference, even if they 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. Keywords: Independent component analysis, blind separation, adaptive filtering cardiac artifacts, ECG analysis.