Objectives: The extraction of the atrial activity in atrial fibrillation episodes is a must for clinical purposes. During atrial fibrillation arrhythmia, the independent atrial and ventricular signals are superposed in the electrocardiogram, fulfilling the independent component analysis (ICA) model. We propose three new algorithms that constrain the classical ICA solution to fit the spectral content of the atrial component. This constraint allows the statement of the problem in terms of semiblind source extraction instead of blind source separation (BSS), in the sense that we only recover one source and we exploit the prior information about the sources in the extraction process.
Methods and materials: The methods used are extensions of classical BSS methods based on second and higher order statistics. We exploit the prior assumption about the sources in order to obtain the source extraction algorithms that are focused on the extraction of the atrial component. The material corresponds to 10 synthetic recordings in order to measure and compare the quality of the different algorithms and 66 real recordings coming from two different databases, one public database from Physionet and one database from the Clinical University Hospital, Valencia, Spain.
Results: We have analyzed the performance of the three new algorithms and compared it with the performance of the traditional ICA algorithms. In the case of the synthetic data, it is possible to obtain the mean square error, so the comparison is easier. The new methods outperform the non-constrained versions in addition to simplifying the solution, since they do not need to recover all the components in order to estimate the atrial activity, i.e., the new methods are focused on the extraction of the atrial activity, so the extraction is stopped after the atrial signal is recovered.
Conclusions: We have shown that the ICA only version of the algorithms can be improved and adapted to fulfill the prior information about the characteristics of the atrial activity. This modification allows us to obtain new algorithms that have the following advantages compared to ICA only based solutions: they exploit prior information during the extraction, not in the postprocessing identification of the atrial signal; they extract only the interesting clinical signal instead of all the components; they outperform the ICA only version of the algorithm, improving the estimation of the atrial signal.