Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
This paper introduces a new method to visualise cardiac fibrillation dynamics termed ‘imputation mapping’. Using a graph neural network approach, global atrial fibrillation dynamics are reconstructed from sparse clinical measurements, achieving superior performance in tracking cardiac arrhythmia patterns compared to existing methods.