Feasibility and performance of methods based on statistical signal processing to study atrial fibrillation

In order to use the ECG as a tool for atrial fibrillation (AF) analysis, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, some statistical signal processing techniques, such as Blind Source Separation (BSS), are able to perform a multi-lead statistical analysis of the ECG with the aim to obtain a set of independent sources where the AA is included. BSS techniques can be divided in two groups depending on the mixing model. Firstly, in algorithms based on Independent Component Analysis (ICA) instantaneous mixture of the sources is assumed. Secondly, in convolutive BSS (CBBS) algorithms the more realistic case of weighted and delayed contributions in the generation of the observed signals is considered. In this paper, a comparison between the performance of ICA algorithms and CBSS algorithms in the extraction of the AA in AF episodes is developed