Po-694-06 convolutional neural networks to identify drivers in persistent atrial fibrillation patients

Identifying driving and initiating areas in persistent atrial fibrillation (AF) might contribute to better understanding AF and improve the ablation treatment. However, their correct identification still requires visual inspection or heavy signal processing algorithms that sometimes distort the electrograms (EGMs) information. In this line, artificial intelligence has recently proven to be a powerful tool that outperforms current detection methods.