期刊
FRONTIERS IN PHYSIOLOGY
卷 13, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fphys.2022.757159
关键词
machine learning; cardiac electrophysiology; atrial fibrillation; Gaussian processes; Riemannian manifolds; active learning
类别
资金
- ANID-FONDECYT Postdoctoral Fellowship [NCN17-129]
- DOE [3190355]
- AFOSR [DE-SC0019116]
- Leading House for Latin American Region [FA9550-20-1-0060, DE-AR0001201]
- Swiss Heart Foundation [RPG 2117]
- SNSF [FF20042, s1074]
- Theo Rossi di Montelera Foundation [197041]
- Metis Foundation Sergio Mantegazza
- Fidinam Foundation
- Horten Foundation
In this study, a multi-fidelity Gaussian process classification method is proposed to efficiently determine inducible regions of arrhythmias in the atria by evaluating the atrial surface. By combining low and high resolution models, this method can predict the ablation sites of atrial fibrillation more accurately.
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.
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