HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks

Understand

Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of more than 3 million people in the U.S.

• and over 33 million people around the world and is associated with a five-fold increased risk of stroke and mortality.
• like other problems in healthcare domain, artificial intelligence (AI)-based algorithms have been used to reliably detect AF from patients' physiological signals.
• The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.