Performance of Optimized Generalized Weighted Estimator ICA algorithm on Biomedical Signals Contaminated by Noise

Abstract

This paper evaluates the performance of OGWE (Optimized Generalized Weighted Estimator) ICA (Independent Component Analysis) algorithm in a biomedical blind source separation problem. Independent signals representing Fetal ECG (FECG) and Maternal ECG (MECG) are generated and then mixed linearly in the presence of white or pink noise to simulate a recording of electrocardiogram. While ICA has been used to extract FECG, very little literature is available on its performance in clinical environment. So there is a need to evaluate performance of these algorithms in Biomedical. To quantify the performance of OGWE algorithm, two scenarios, i.e., (a) different amplitude ratios of simulated maternal and fetal ECG signals, (b) different values of additive white Gaussian noise or pink noise, were investigated. Higher order and second order performances were measured by performance index and signal-to-error ratio respectively. The selected ICA algorithm separates the white and pink noises equally well. This paper reports on the performance of the ICA algorithm.