MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
This work addresses the need for interpretable models in clinical ECG diagnosis, though it is incremental as it builds on existing deep learning approaches.
The authors tackled the problem of predicting heart diseases from ECG signals with interpretable deep learning models, achieving a PR-AUC of 0.9436, which outperformed the best baseline by 5.51%.
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.