  Electrocardiography (ECG) signals are commonly used to diagnose various
cardiac abnormalities. Recently, deep learning models showed initial success on
modeling ECG data, however they are mostly black-box, thus lack
interpretability needed for clinical usage. In this work, we propose MultIlevel
kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG
signals with intuitive explanation aligned with medical knowledge. By
extracting multilevel (beat-, rhythm- and frequency-level) domain knowledge
features separately, MINA combines the medical knowledge and ECG data via a
multilevel attention model, making the learned models highly interpretable. Our
experiments showed MINA achieved PR-AUC 0.9436 (outperforming the best baseline
by 5.51%) in real world ECG dataset. Finally, MINA also demonstrated robust
performance and strong interpretability against signal distortion and noise
contamination.
