SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection

arXiv:2310.09203v2 Announce Type: replace
Abstract: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is associated with an increased risk of stroke, heart failure, and other cardiovascular complications, but can be clinically silent. Passive AF monitoring with wearables may help reduce adverse clinical outcomes related to AF. Detecting AF in noisy wearable data poses a significant challenge, leading to the emergence of various deep learning techniques. Previous deep learning models learn from a single modality, either electrocardiogram …

abstract arxiv clinical cs.ai cs.lg detection failure heart failure information monitoring reduce risk robust stroke type wearables