Atrial Fibrillation Detection

Atrial fibrillation (AF) detection research focuses on developing accurate and efficient methods for identifying this common and dangerous heart rhythm irregularity, primarily using electrocardiograms (ECGs) and photoplethysmography (PPG) signals. Current efforts concentrate on improving the accuracy and robustness of deep learning models, including convolutional neural networks (CNNs) and vision transformers, while addressing challenges like noise sensitivity and the need for interpretability in diagnostic tools. These advancements aim to enable more accessible and reliable AF detection through wearable devices and mobile phone applications, ultimately improving patient care and reducing the burden of this condition.