Comparison of JADE and Canonical Correlation Analysis for ECG de-noising

This paper explores differences between two methods for Blind Source Separation within frame of ECG de-noising. First method is Joint Approximate Diagonalization of Eigenmatrices, which is based on estimation of fourth order cross-cummulant tensor and its diagonalization. Second one is the statistical method known as Canonical Correlation Analysis, which is based on estimation of correlation matrices between two multidimensional variables. Both methods were used within method, which combines the Blind Source Separation algorithm with decision tree. The evaluation was made on large database of 382 long-term ECG signals and the results were examined. Biggest difference was found in results of 50 Hz power line interference where the CCA algorithm completely failed. Thus main power of CCA lies in estimation of unstructured noise within ECG. JADE algorithm has larger computational complexity thus the CCA perfomend faster when estimating the components.