A Fast and Generalizable Deep Neural Network for the Detection of Atrial Fibrillation

Abstract

Atrial fibrillation (AFib) is the most prevalent sustained cardiac arrhythmia, yet most deep learning detectors are validated on a single cohort and few report uncertainty. We developed a compact 14M-parameter convolutional neural network for binary AFib classification from 10 s, 12-lead ECG at 500 Hz, trained exclusively on the HDXML dataset (67,432 ECGs; 9628 AFib) with on-the-fly augmentation for noise, drift and missing channels. Using one unchanged checkpoint, we evaluated six independently sourced public cohorts totaling over 1.25 million recordings, including MIMIC-IV critical care and two ambulatory Holter cohorts. All metrics are reported with 95% confidence intervals from a cluster bootstrap at the patient, recording or subject levels. ROC–AUC exceeded 0.95 in every cohort, from 0.960 (95% CI 0.956–0.963) on CODE-15 to 0.999 (0.998–0.999) on SPH, including 0.966 (0.965–0.967) on MIMIC-IV with 75.3% sensitivity and 97.7% specificity, 0.963 (0.929–0.987) on CPSC 2021 and 0.995 (0.988–0.999) on MIT-BIH. Performance was stable under progressive lead masking without retraining, and median inference took 110 ms per segment on a server-class CPU without a GPU. A compact network trained on one curated source can therefore generalize across hospitals, countries and recording hardware. This study is retrospective; prospective validation is required before clinical use.