Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

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.

Methods

We analysed 35 634 12-lead ECG recordings from three public databases (China Physiological Signal Challenge-Extra, PTB-XL and Georgia), each with physician-validated AF labels. A range of convolutional neural network models, including AlexNet, VGG-16, ResNet and transformers, were tested for AF prediction, enriched with HRV and demographic data to explore the effectiveness of the multimodal approach. Each data m

SUBMITTER: Rawshani A 

PROVIDER: S-EPMC11962809 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature