Why the study?
The exact mechanisms of AF have remained elusive, and the complexity, irregular period, and non-local nature of atrial signals make characterising its spatiotemporal organisation very difficult.
Population
10 intracardiac recordings from patients with paroxysmal or persistent AF during ablation procedures
Key result
A synchronization theory-based algorithm classified 10 atrial fibrillation recordings into three types: single driver (n=3), weakly coupled drivers (n=4), and moderately interacting drivers (n=3).
Authors
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Enables AF classification by driver interactions; hypothesis-generating for mechanism-based therapies pending validation.
Observational (n=10)
A novel data-processing pipeline based on synchronization theory can classify human atrial fibrillation into distinct spatiotemporal types based on driver interactions, providing mechanistic insights.
Shahriar Iravanian (2021) conducted an observational in Atrial Fibrillation (n=10). Data-processing tools based on synchronization theory was evaluated on Classification of AF driver types. A synchronization theory-based algorithm classified 10 atrial fibrillation recordings into three types: single driver (n=3), weakly coupled drivers (n=4), and moderately interacting drivers (n=3).
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