Comparing RR-Interval-Based and Whole-Signal-Based Machine Learning Models for Atrial Fibrillation Detection from Single-lead Electrocardiograms

作者：Zixuan Ding, Jonathan Mant, James Brimicombe, Tommaso Bucci, Benjamin J. R. Buckley, Peter Calvert, Wern Yew Ding, Andrew Dymond, Gregory Y.H. Lip, Riccardo Proietti, Kate Williams, E. Punskaya, Peter Charlton · 发表于：Computing in cardiology · 年份：2024 · DOI：10.22489/cinc.2024.059 · 被引用次数：1 · 研究领域：ECG Monitoring and Analysis

The aim of this study was to compare the performance of machine learning models to detect atrial fibrillation (AF) from single-lead ECGs which use either RR-intervals alone, or the entire ECG signal.Experiments were conducted using single-lead, 30-second ECG signals acquired using handheld ECG recorders from two datasets: the Computing in Cardiology (CinC) 2017 dataset (public), and the Screening for Atrial Fibrillation with ECG to Reduce Stroke (SAFER) dataset (private).The models assessed in this study were: two models which used the whole ECG signal, both of which were top-performing models from the 2017 PhysioNet / CinC Challenge; and two RR-interval-based models -a state-of-the-art model and a novel model which detects AF from a 2D representation of the differences between RR intervals.The models had AUROCs of 0.93 -0.99.The AUPRCs varied more widely, from 0.64-0.94.The novel RR-interval-based AF detection model achieved an AUPRC of 0.94 on the CinC 2017 dataset, outperforming the state-of-the-art RRinterval-based model (0.88) and the entire-signal-based models (0.68 and 0.64).This experiment demonstrated that AF detection models utilizing only RR intervals could achieve comparable performance to those utilizing the entire ECG signal.