Enhanced Detection of Atrial Fibrillation in Single-lead Electrocardiograms Using a Cloud-based Artificial Intelligence Platform

Background: Although smartphone-based devices have been developed to record 1-lead electrocardiogram (ECG), existing solutions for automatic detection of atrial fibrillation (AF) often has poor positive predictive value.

Objective: This study aimed to validate a Cloud-based deep-learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records.

Methods: We analyzed 8528 patients with 30-second ECG records from a single-lead handheld ECG device. Ground truth for AF presence was established through a benchmark algorithm and expert manual labeling. The Willem Artificial Intelligence (AI) platform, not trained on these ECGs, was used for automatic arrhythmia detection, including AF. A rules-based algorithm was also used for comparison. An expert cardiology committee reviewed false positives and negatives, and performance metrics were computed.

Results: The AI platform achieved an accuracy of 96.1% (initial labels) and 96.4% (expert review), with sensitivities of 83.3% and 84.2%, and specificities of 97.3% and 97.6%, respectively. The positive predictive value was 75.2% and 78.0%, and the negative predictive value was 98.4%. Performance of the AI platform largely exceeded the performance of the rules-based algorithm for all metrics. The AI also detected other arrhythmias, such as premature ventricular complexes, premature atrial complexes along with 1-degree atrioventricular blocks.

Conclusion: The result of this external validation indicates that the AI platform can match cardiologist-level accuracy in AF detection from 1-lead ECGs. Such tools are promising for AF screening and have the potential to improve accuracy in noncardiology expert health care professional interpretation and trigger further tests for effective patient management.