Supraventricular tachyarrhythmias, in particular Atrial Fibrillation (AF), are the most commonly cardiac diseases encountered in the routine clinical practice. The prevalence of AF is less than 1% among population under 60 years old, but it increases significantly among those over 70, approximating to 10% in those older than 80. Undergoing a sutained AF episode is related to a higher mortality ratio and to a rising probability of suffering thromboembolisms, myocardial infarction, and stroke. On the other hand, paroxysmal AF (PAF), which is characterized by its spontaneous termination, is frequently the precursor to sustained AF. This provokes a great interest among the scientific community in disclosing the mechanisms which lead to AF perpetuation or to spontaneous AF termination.The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of cardiac pathologies. In order to use the ECG as a tool for the AF study, the atrial activity (AA) must be separated from other cardioelectric signals. In this sense, Blind Source Separation (BSS) techniques are able to perform a multi-lead statistical analysis with the aim to obtain a set of independent sources that include the AA. When the BSS problem is tackled, it becomes necessary to consider a source mixing model near to the real mixing process in order to develop mathematical algorithms that solve the problem. A feasible model consists of assuming the linear mixture of sources. Within this linear mixing model it can be made the additional assumption of instantaneous mixture. This instantaneous linear mixing model is the one used in Independent Component Analysis (ICA). An alternative mixing model is considered by convolutive BSS (CBSS) algorithms, where a more realistic process in the generation of ECG leads is taken into account with delayed contributions of cardioelectric sources.In this thesis, a performance study of CBSS algorithms applied to AA extraction from ECG recordings has been carried out for the first time. With this aim, the most relevant CBSS algorithms have been compared with the instantaneous algorithm FastICA, the effectivity of which is extensively proved. This comparison will allow to know which CBSS algorithms are useful for AA extraction from ECG recordings of AF episodes. On the other hand, CBSS algorithms have the problem of requiring a minimum number of observed signals for their suitable vi ABSTRACT application. Here a new AA extraction algorithm is presented, which is based on the convolutive mixing model and solves the problem of lack of available leads from Holter ECG recordings. The high likeliness level between original and estimated AA, measured by different performance indicators, demonstrates the suitability of this new method for the AA extraction from Holter recordings and, furthermore, a higher robustness against noise of the convolutive mixing model is highlighted.The most common cause of undergoing an AF episode is attributed to the reentry mechanism, which consists o...

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