An Atrial Fibrillation Detection Strategy Based on Self-Supervised Pretraining in Wearable ECG Monitoring

Atrial fibrillation (AF) is an insidious cardiac arrhythmia, with its incidence increasing annually. Timely screening and home-based interventions play a vital role in the effective management of AF. Within the Internet of Medical Things (IoMT) landscape, wearable electrocardiogram (ECG) monitoring devices have been seamlessly integrated to monitor AF. Nonetheless, the substantial influx of ECG signals awaiting annotation and the exorbitant costs associated with employing specialists for manual annotation pose significant hurdles in the development of AF detection systems. Despite the potential utilization of AF detectors trained on open databases, their efficacy in analyzing continuous wearable ECGs remains inadequate. Self-supervised representation learning proficiently characterizes unlabeled data and enhances data utilization without labels. This study proposes an AF detection strategy based on self-supervised pretraining, aiming for optimal AF detection performance with minimal annotation costs. The proposed method employed self-supervised representation learning and pretraining strategy and was validated on four datasets from the fourth China Physiological Signal Challenge 2021 (CPSC2021) database, achieving accuracies of 98.13%, 90.11%, 92.63%, and 91.29%. In addition, validation on 20 wearable ECG recordings yielded mean accuracies of 99.38% and 99.48% for AF and Non-AF on unlabeled data from recordings used for fine-tuning, respectively. We achieved mean accuracies of 97.19% and 94.15% for AF and Non-AF on independent recordings, respectively. The results demonstrate the proposed method's reliability for AF detection in wearable ECG monitoring.