MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals

Understand

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.