Probabilistic source separation for robust electrocardiography

Blind source separation (BSS) techniques are widely used to extract signals of interest from a mixture with other signals. These methods, however, typically lack possibilities to incorporate any prior knowledge on the mixing of the source signals. Particularly for electrocardiographic signals, knowledge on the mixing is available based on the origin and propagation properties of these signals. In this paper, a novel source separation method is developed that combines the strengths and accuracy of BSS techniques with the robustness of an underlying physiological model of the electrocardiogram (ECG). The method is developed within a probabilistic framework and yields an iterative convergence of the separation matrix towards a maximum a posteriori estimation, where in each iteration the latest estimate of the separation matrix is corrected towards the physiological model. The method is evaluated by comparing its performance to that of FastICA on both simulated and real multi-channel ECG recordings, demonstrating that the developed method outperforms FastICA in terms of extracting the ECG source signals