Why the study?
Can CNN-LSTM networks using body surface potentials accurately estimate atrial fibrillation driver position non-invasively?
Can CNN-LSTM networks using body surface potentials accurately estimate atrial fibrillation driver position non-invasively?
CNN-LSTM networks using body surface potentials offer a promising non-invasive strategy for estimating atrial fibrillation driver positions by exploiting temporal correlations.
Hypothesis-generating for non-invasive AF driver mapping; leaves open prospective clinical validation before any practice change.
Good results in R1 are highly convenient since AF drivers are commonly found in this area: the left atrial appendage, as suggested in some previous studies. These promising results indicate that using CNN-LSTM networks could lead to new strategies exploiting temporal correlations to address this challenge effectively.
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Fernández et al. (2024) studied this question.
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