This paper evaluates the performance of some major Independent Component Analysis (ICA) algorithms like Cardoso’s Joint Approximate Diagonalization of Eigen matrices (JADE), Comon’s algorithm and Optimized Generalized Weighted Estimator (OGWE) ICA 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 ICA algorithms, 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 algorithms separate the white and pink noises equally well. This paper reports on the performance of the ICA algorithms.

BSS ICA Biomedical Signal Processing