  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. In other words, these
approaches are unable to explain the reasons behind their decisions. The lack
of interpretability is a common challenge toward a wide application of machine
learning-based approaches in the healthcare which limits the trust of
clinicians in such methods. To address this challenge, we propose HAN-ECG, an
interpretable bidirectional-recurrent-neural-network-based approach for the AF
detection task. The HAN-ECG employs three attention mechanism levels to provide
a multi-resolution analysis of the patterns in ECG leading to AF. The first
level, wave level, computes the wave weights, the second level, heartbeat
level, calculates the heartbeat weights, and third level, window (i.e.,
multiple heartbeats) level, produces the window weights in triggering a class
of interest. The detected patterns by this hierarchical attention model
facilitate the interpretation of the neural network decision process in
identifying the patterns in the signal which contributed the most to the final
prediction. Experimental results on two AF databases demonstrate that our
proposed model performs significantly better than the existing algorithms.
Visualization of these attention layers illustrates that our model decides upon
the important waves and heartbeats which are clinically meaningful in the
detection task.
