Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.

ABSTRACT:

Background

Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.

Methods

Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm² resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm² resolution, respectively). Unipolar MEM and NIOM recordings were p

SUBMITTER: Zolotarev AM 

PROVIDER: S-EPMC7577986 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature