Abstract 14900: Human Atrial Fibrillation Drivers Revealed by Machine Learning Using Multicomponent Electrograms and 3D CE-MRI Structural Features

Abstract

Introduction: Conventional multielectrode mapping is not sufficient to reveal subsurface intramural activation. Thus, atrial fibrillation (AF) driver identification remains challenging. To overcome these limitations we utilized machine learning (ML) to identify AF drivers based on the combination of electrogram (EGM) and 3D structural magnetic resonance imaging (MRI) features. Hypothesis: Detailed electrogram features analysis, including minor deflections, combined with local structural features, can be used to define AF driver. Methods: Sustained AF was mapped in coronary perfused explanted human atria (n=7) with near-infrared optical mapping (NIOM) (0.3-0.9mm 2 resolution) and 64-electrode mapping catheter (3mm 2 resolution). Unipolar EGMs were analyzed for multiple features of the steepest negative deflection and the 2nd-4th steepest deflections in multicomponent EGMs. Atria underwent 9.4T MRI (154-180μm 3 resolution) with gadolinium enhancement and histology validation of fibrosis. Both 3D structural and EGM data from NIOM defined driver and non-driver regions were processed by ML algorithms (LR; PLSDA; GBM; CRF; PSVM; RSVM) using double cross-validation. Results: AF drivers’ reentrant tracks were defined by NIOM activation mapping, the gold-standard, and confirmed by targeted ablation. The best performing ML algorithm (PLSDA) correctly classified mapped driver region with 76.1% accuracy on the testing data. The most important features included sub-endocardial fibrosis, sub-epicardial fiber orientation, local wall thickness, beat-to-beat variability of multicomponent EGM deflections. Conclusions: The ML models pre-trained on combined EGM and structural features allow efficient classification of AF driver vs non-driver regions defined by the NIOM gold-standard. The results suggest that AF driver substrates formed by the combination of 3D fibrotic structural features, which correlate with local EGM characteristics.