Abstract 14738: Classification of Atrial Fibrillation by Deep Learning of Electrogram Shapes versus Rate and Regularity

Abstract

Introduction: It is unclear whether atrial fibrillation (AF) is best identified on intracardiac recordings by varying shape, rapid rate or extent of irregularity. Prioritizing these features may improve device diagnosis of AF. Hypothesis: AF can be separated from organized atrial flutter (AFL) by electrogram shape, independent of the contributions of rate or regularity. Methods: In 86 patients (25 female, age 65±11 years) we trained a convolutional neural network (CNN) to classify AF or AFL from 64 unipolar electrograms of persistent AF recorded for 60 seconds. In cases labelled as AF, we modified inputs by progressively regularizing (a) electrogram shape; (b) rate or (c) regularity in timing, to define which switched the classification to AFL. Results: The CNN provided a c-statistic of 0.95 ± 0.05 to identify AF or AFL in independent test cohorts not used for training, using 10-fold cross validation. Fig A shows AF in which progressive regularization of shape and timing from #1 to #4 flipped CNN classification into AFL in 45%. EGMs with 100% consistent shape and timing were classified by their cycle length (CL=1/rate): ~90% AF for CL < 175 ms, ~80% AFL for CL from 200-280 ms. Fig. B shows sequences simulated from patient-specific EGMs of AF that were classified as AF in 91 ± 12% of cases even if regular with CL of 200-280 ms, showing AF classification based on EGM shape alone. Figure B illustrates some ‘AF pathognomic’ electrogram shapes in red. Conclusions: AF may be identified by specific EGM shape patterns independent of regularity or rate. Regularity in shape and timing contribute ~45% to AFL classification, and adding CL explains up to 80%. Studies are required to study the mechanistic basis and clinical implications of specific AF-electrogram shapes.