Introduction: A quantifiable, automated standard of analyzing heart rhythm has long eluded cardiologists due, in part, to the limitations in technology and the ability to analyze large electrogram datasets. In this proof-of-concept study, we propose new measures to quantify plane activity in atrial fibrillation (AF) using our Representation of Electrical Tracking of Origin (RETRO)-Mapping software.
Methods: We recorded 30 s segments of electrograms at the lower posterior wall of the left atrium using a 20-pole double loop catheter (AFocusII). The data were analyzed with the custom RETRO-Mapping algorithm in MATLAB. Thirty second segments were analyzed for number of activation edges, conduction velocity (CV), cycle length (CL), activation edge direction, and wavefront direction. These features were compared across 34 613 plane edges in three types of AF: persistent AF treated with amiodarone (11 906 wavefronts), persistent AF without amiodarone (14 959 wavefronts), and paroxysmal AF (7748 wavefronts). Change in activation edge direction between subsequent frames and change in overall wavefront direction between subsequent wavefronts were analyzed.
Results: All activation edge directions were represented across the lower posterior wall. The median change in activation edge direction followed a linear pattern for all three types of AF with R2 = 0.932 for persistent AF treated without amiodarone, R2 = 0.942 for paroxysmal AF, and R2 = 0.958 for persistent AF treated with amiodarone. All medians and the standard deviation error bars remained below 45° (suggesting all activation edges were traveling within a 90° sector, a criterion for plane activity). The directions of approximately half of all wavefronts (56.1% for persistent without amiodarone, 51.8% for paroxysmal, 48.8% for persistent with amiodarone) were predictive of the directions of the subsequent wavefront.
Conclusion: RETRO-Mapping can measure electrophysiological features of activation activity and this proof-of-concept study suggests that this can be extended to the detection of plane activity in three types of AF. Wavefront direction may have a role in future work for predicting plane activity. For this study, we focused more on the ability of the algorithm to detect plane activity and less the differences between the types of AF. Future work should be in validating these results with a larger data set and comparing with other types of activation such as rotational, collision, and focal. Ultimately, this work can be implemented in real-time for prediction of wavefronts during ablation procedures.
Keywords: arrhythmia; atrial fibrillation; mapping; mechanisms.
© 2023 The Authors. Journal of Cardiovascular Electrophysiology published by Wiley Periodicals LLC.