Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis.

Tiago P Almeida,Xin Li,Gavin S Chu,G André Ng,Jiun H Tuan,João Salinet,Peter J Stafford,Diogo C Soriano,Fernando S Schlindwein

doi:10.1063/1.5024248

Abstract

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.

Highlights

The dynamics of cardiac signals in the presence of arrhythmias have been extensively investigated in the context of recurrence analysis, especially when considering the main advantages of recurrence plots (RPs) and recurrence quantification analysis (RQA) for characterizing short time series, phase transitions, nonstationarity and unveiling nonlinear underlying phenomena in general [1,2,3,4,5,6,7]
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 persistent AF (persAF) patients undergoing a clinical procedure for atrial fibrillation (AF) therapy
For the calculation of ε, the area under the Receiver operating characteristic (ROC) (AUROC) curves – shown in Table 1 – suggest that 2% of the maximum phase space diameter represents a good compromise among the resulting recurrence rate (RR), the discrimination between normal and fractionated AEGs (Figure 3A), and the portion of the maximum phase space diameter

Summary

Introduction

The dynamics of cardiac signals in the presence of arrhythmias have been extensively investigated in the context of recurrence analysis, especially when considering the main advantages of recurrence plots (RPs) and recurrence quantification analysis (RQA) for characterizing short time series, phase transitions, nonstationarity and unveiling nonlinear underlying phenomena in general [1,2,3,4,5,6,7]. It is important to emphasize, that the experimental detection of chaotic phenomena usually exhibits drawbacks as a consequence of additive (measurement) noise, unstable experimental conditions and the requirements for a large amount of data for suitable statistical characterization as occurs, for instance, in the classical Grassberger & Proccacia (GP) algorithm for correlation dimension estimation [19]. These drawbacks motivate the employment of different strategies for characterizing the fibrillation behavior and its possible nonlinear nature and justify the use of RQA due to its intrinsic features previously mentioned

Methods

Results

Discussion

Conclusion