Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all
treatment with limited success in persistent AF. This may be due to our
inability to map the dynamics of AF with the limited resolution and coverage
provided by sequential contact mapping catheters, preventing effective patient
phenotyping for personalised, targeted ablation. Here we introduce FibMap, a
graph recurrent neural network model that reconstructs global AF dynamics from
sparse measurements. Trained and validated on 51 non-contact whole atria
recordings, FibMap reconstructs whole atria dynamics from 10% surface coverage,
achieving a 210% lower mean absolute error and an order of magnitude higher
performance in tracking phase singularities compared to baseline methods.
Clinical utility of FibMap is demonstrated on real-world contact mapping
recordings, achieving reconstruction fidelity comparable to non-contact
mapping. FibMap's state-spaces and patient-specific parameters offer insights
for electrophenotyping AF. Integrating FibMap into clinical practice could
enable personalised AF care and improve outcomes.
