Self-Supervised ECG Foundation Models for Enhanced Detection of Paroxysmal Atrial Fibrillation

Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification

Recent advances in deep learning have led to the development of ECG foundation models (ECG-FMs) trained with self-supervised learning, which can extract generalizable representations from large-scale data. In this study, we evaluated the performance of these ECG-FMs in detecting atrial fibrillation and sinus rhythm dur...

Forecasting and Detection of Paroxysmal Atrial Fibrillation from Electrocardiograms Using Transparent Deep Learning Models

Detection of Paroxysmal Atrial Fibrillation from Sinus Rhythm Using 96-Lead Electrocardiogram and Graph Neural Networks

Early Detection of Cardiac Arrhythmia Using Bi-LSTM Networks on ECG Signals

Cardiovascular diseases (CVDs) are a top global cause of death. Early detection of cardiac abnormalities using non-invasive techniques, such as electrocardiogram (ECG) signals, plays a significant role in improving patient outcomes and reducing mortality. Traditional methods rely on handcrafted feature extraction, whic...

AI-Enabled Atrial Fibrillation Detection, Prediction, and Classification Based on ECG Data: A Review

This review systematically examines AI-based approaches for AF analysis, evaluating their advantages and limitations using key performance metrics, including accuracy, sensitivity, specificity, and F-score.

An Explainable, Demographic-Aware ECG Transformer for Atrial Fibrillation Detection

Abstract Deep learning models have demonstrated strong performance in electrocardiogram (ECG)-based cardiovascular disease detection, but face the challenge of systematic variation across individual patients. Patient-specific information, a cornerstone of precision medicine, may help models account for this variability...