Surface ECG organization time course analysis along onward episodes of paroxysmal atrial fibrillation

Autores UPV
Año
Revista MEDICAL ENGINEERING & PHYSICS

Abstract

The complete understanding of the mechanisms leading to the initiation, maintenance and self-termination of atrial fibrillation (AF) still is an unsolved challenge for cardiac electrophysiology. Studies in which AF has been induced have shown that electrophysiological and structural remodeling of the atria during the arrhythmia could play an important role in the transition from paroxysmal to persistent AF. However, to this day, the time course of the atrial remodeling along onward episodes of non-induced paroxysmal AF has not been investigated yet. In this work, a non-invasive method, based on the regularity estimation of AF through sample entropy (SampEn), has been used to assess the organization evolution along onward episodes of paroxysmal AF. Given that AF organization has been associated to the number of existing wavelets wandering throughout the atrial tissue, SampEn could be considered as a concomitant estimator of atrial remodeling. The achieved results, in close agreement with previous findings obtained from invasive recordings, proved several relevant aspects of arial remodeling. Firstly, a progressive disorganization increase (SampEn increase) along onward episodes of AF has been observed for 63% of the analyzed patients, whereas a stable AF organization degree has been appreciated in the remaining 37%. Next, a positive correlation between episode duration and SampEn has been obtained (R = 0.541, p< 0.01). Finally, a remarkable influence of the fibrillation-free interval, preceding each episode, on the corresponding level of AF organization at the onset of the subsequent AF episode has been observed, with a correlation between these two indices of R = 0.389 (p< 0.01). As a consequence, it could be considered that atrial electrophysiological dynamics that occur along onward paroxysmal AF episodes are reflected and can be quantified from ECG recordings through non-invasive organization estimation. © 2010 IPEM.