Why the study?
Ablation for persAF is challenging because there is no ground truth for atrial substrate characterization and multiple mechanisms drive the arrhythmia.
Unsupervised machine learning classification of atrial electrograms identified five distinct classes of fractionation, potentially improving substrate characterization for persistent AF ablation.
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May refine AEG substrate mapping in persistent AF; leaves open whether class-guided ablation improves outcomes.
Almeida et al. (2020) studied this question.
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