MINA: Multilevel Knowledge-Guided Attention for Modeling
Electrocardiography Signals
Abstract
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