Electrocardiogram Variations in the Prediction of Development of Atrial Fibrillation

Atrial fibrillation (AF) is the most common arrhythmia, affecting 1% of adults, with its prevalence increasing with age \[1\]. It is associated with increased morbidity and mortality. Most patients who develop AF have architectural and anisotropic micro changes in the atrial myocardium. These cause heterogeneous and discontinuous changes in the patterns of impulse propagation, heterogeneous atrial activation and shortening of atrial refractory period \[2, 3\]. Since 1911 the standard EKG \[4\] (approximately 15 seconds of recording and bandwidth 0.05 to 150 Hz) is the most used tool for the evaluation of patients with arrhythmias, due to its low cost and high availability. Various electrocardiographic patterns are known predictors of AF as evidenced by direct visual inspection. For example, prolongation of P wave duration during sinus rhythm would correlate with structural changes such as increasing the size of the left atrium (the increase in left atrial pressure) or a decrease in driving time \[5\]. These changes favor the development of reentry circuits responsible for the development and maintenance of AF. The registration of the electrocardiographic activity provides much more information than evidenced by direct visual inspection. Biosignal processing of these specific techniques to detect potential delays caused by abnormal conduction of the myocardium that favor re-entry mechanisms \[6-10\]. For this purpose prolonged ECG with 1000 Hz sampling frequency. Knowledge about the prediction of the development of AF with the standard ECG is not obvious on visual inspection is limited. Many of these structural changes and anisotropic, occur slowly over time and may be evidenced by direct variations between 2 ECG from the same individual \[11\]. Little is known about whether differences in morphology, axis, scope or duration of P wave related to these structural changes may predict the development of AF. Our main purpose is to evaluate the prognostic performance of a set of parameters as evidenced by direct inspection of the ECG, and ECG changes from 2 to predict the FA development. The design of this tool could allow future generation of software capable of identifying and reporting these variations, most useful prognostic risk in patients with AF.