Key result
Deep learning locates ~93% of AF drivers from simulated body surface potentials.
Why the study?
Intracardiac mapping has limited spatial resolution for identifying AF drivers, while noninvasive ECGI requires complex mathematical modeling of the atria and torso.
Can deep learning accurately localize atrial fibrillation drivers from body surface potentials in computerized models?
Can deep learning accurately localize atrial fibrillation drivers from body surface potentials in computerized models?
Deep learning applied to body surface potentials can accurately localize atrial fibrillation drivers in computerized models, offering a potential non-invasive alternative to traditional mapping.
Premature for clinical AF mapping; leaves open in vivo validation of deep learning localization from body surface potentials.
Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns. Multipoint intracardiac mapping systems present a limited spatial resolution, which makes it difficult to identify AF drivers and ablation targets. These AF onset locations and drivers responsible for AF perpetuation are main targets for ablation procedures. Although noninvasive electrocardiographic imaging (ECGI) and inverse problem-based methods have been tested during AF conditions, they need an accurate mathematical modeling of atria and torso to get good results. In this work, we propose to model the location of AF drivers from body surface potentials (BPS) as a supervised classification problem. We used deep learning techniques to address the problem. We were able to correctly locate the 92% and 96% of drivers in the test and training sets, respectively (accuracy of 0.92 and 0.96), while the Cohen's Kappa was 0.89 for both sets. Therefore, proposed method can help to identify target regions for ablation using a noninvasive procedure as BSP mapping.
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Cámara-Vázquez et al. (2020) studied Atrial Fibrillation (n=13). Deep Learning (Multilayer Perceptron) for AF driver localization was evaluated on Accuracy of AF driver localization in the test set. A deep learning approach using a Multilayer Perceptron correctly located 92.5% of atrial fibrillation drivers from simulated body surface potentials in the test set.
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