Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.
Journal:
Open heart
PMID:
40164487
Abstract
BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the risk of stroke and heart failure. Early detection is crucial, yet traditional methods struggle with AF's transient nature. This study investigates how augmenting ECG data with heart rate variability (HRV) and demographic data (age and sex) can improve AF detection.