A Neural Network for Atrial Fibrillation Detection via PPG.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with severe complications such as ischemic stroke and heart failure. Early detection is essential for timely intervention; however, traditional diagnostic methods often lack scalability and accessibility. This project explores the use of photoplethysmography (PPG) signals recorded via smartphone applications to develop a predictive neural network model for AF detection.