Abstract 14443: Deep Learning to Identify Atrial Fibrillation Phenotypes in Individual Patientsbased on Rate, Variability, Electrogram Shape

Abstract

Introduction: Automatic identification of atrial fibrillation (AF) from Flutter or Tachycardia (AFL/AT) is key to therapy, yet devices often group them as ‘high-rate events’. Hypothesis: We hypothesized that (a) convolutional neural networks (CNN) may separate AF from AFL/AT better than Electrogram (EGM) rate or rhythm, and (b) The physiology represented by CNN could be probed using novel methods including computer modelling. Methods: We developed custom CNN on N= 450,960 EGM tracings (4 sec), in 110 patients with AF(n=55) or AFL/AT (n=55) at ablation (31.8% female, 65±11Y). Training used 88 patients; validation used 22. We systematically probed CNN with 140,360 EGM sequences controlling rate (cycle length, CL), beat-to-beat variability (%) or shape (autocorrelation, %) (figs A-B) alone or in combination. Results: CNN differentiated AF from AFL with a c-statistic of 0.95. Individual variations in rate (N=572), CL variability (N=418), shape (N=462), or in combination (N=26,620) decreased the c-statistic of the CNN predictions to 0.66, 0.84, 0.88, 0.58 ( P <0.05, Delong test) respectively, suggesting rate as the primary determinant of CNN decision (Figure 1, C). Conclusions: Machine learning of intracardiac EGMs separates AF from AFL/AT significantly better than electrogram rate, regularity or shape. Opening the ‘black-box’ of CNN showed that additional indices from Electrograms identify the physiology of AF in individual patients. Studies on the physiological signatures for AF between patients may identify important clinical phenotypes.