Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis

Atrial fibrillation (AF) is regarded as a complex arrhythmia, with one or more co-existing mechanisms, resulting in an intricate structure of atrial activations. Fractionated atrial electrograms (AEGs) were thought to represent arrhythmogenic tissue and hence have been suggested as targets for radiofrequency ablation. However, current methods for ablation target identification have resulted in suboptimal outcomes for persistent AF (persAF) treatment, possibly due to the complex spatiotemporal dynamics of these mechanisms. In the present work, we sought to characterize the dynamics of atrial tissue activations from AEGs collected during persAF using recurrence plots (RPs) and recurrence quantification analysis (RQA). 797 bipolar AEGs were collected from 18 persAF patients undergoing pulmonary vein isolation (PVI). Automated AEG classification (normal vs. fractionated) was performed using the CARTO criteria (Biosense Webster). For each AEG, RPs were evaluated in a phase space estimated following Takens' theorem. Seven RQA variables were obtained from the RPs: recurrence rate; determinism; average diagonal line length; Shannon entropy of diagonal length distribution; laminarity; trapping time; and Shannon entropy of vertical length distribution. The results show that the RQA variables were significantly affected by PVI, and that the variables were effective in discriminating normal vs. fractionated AEGs. Additionally, diagonal structures associated with deterministic behavior were still present in the RPs from fractionated AEGs, leading to a high residual determinism, which could be related to unstable periodic orbits and suggesting a possible chaotic behavior. Therefore, these results contribute to a nonlinear perspective of the spatiotemporal dynamics of persAF. Biological markers that better explain atrial fibrillation (AF) behavior and provide a definitive answer for persistent atrial fibrillation (persAF) therapy are still in debate due to its complex underlying pathophysiology and spatiotemporal behavior. As such, the role of low dimensional structures for explaining AF has been the subject of many investigations, showing that recurrence quantification analysis (RQA) might be useful to explore the underlying AF dynamics. However, a consistent set of RQA variables taking into account the specificities of the signals and of the theoretical methodology is still needed. In the present work, we propose rigorous steps for a proper reconstruction of the recurrence plots (RPs) and for the estimation of RQA-based variables extracted from atrial electrograms (AEGs) collected from persAF patients undergoing a clinical procedure for AF therapy. We demonstrate that these RQA-based variables are sensitive to important electrophysiologic characteristics of the atrial tissue and could potentially be used as biological markers to guide the clinical procedure. Additionally, a high residual determinism was found in the RPs from AEGs with seemingly turbulent characteristics, which implies that the spatiotemporal dynamics of persAF mechanisms is not necessarily associated to a random structure.