Convolutive Blind Source Separation Algorithms Applied to the Electrocardiogram of Atrial Fibrillation: Study of Performance

The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of atrial fibrillation (AF). In order to use the ECG as a tool for the analysis of AF, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, statistical signal processing techniques, like blind source separation (BSS), are able to perform a multilead statistical analysis with the aim to obtain the AA. Linear BSS techniques can be divided in two groups depending on the mixing model: algorithms where instantaneous mixing of sources is assumed, and convolutive BSS (CBSS) algorithms. In this work, a comparison of performance between one relevant CBSS algorithm, namely Infomax, and one of the most effective independent component analysis (ICA) algorithms, namely FastICA, is developed. To carry out the study, pseudoreal AF ECGs have been synthesized by adding fibrillation activity to normal sinus rhythm. The algorithm performances are expressed by two indexes: the signal to interference ratio (SIR_AA) and the cross-correlation (R_AA) between the original and the estimated AA. Results empirically prove that the instantaneous mixing model is the one that obtains the best results in the AA extraction, given that the mean SIR_AA obtained by the FastICA algorithm (37.6 plusmn 17.0 dB) is higher than the main SIR_AA obtained by Infomax (28.5 plusmn 14.2 dB). Also the R_AA obtained by FastICA (0.92 plusmn 0.13) is higher than the one obtained by Infomax (0.78 plusmn 0.16).