Papers › MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Atrial Fibrillation Detection | PhysioNet Challenge 2017 | MINA | F1 | 0.8342 | #2 of 2 | Archive leaderboard | report |
| Atrial Fibrillation Detection | PhysioNet Challenge 2017 | MINA | PR-AUC | 0.9436 | #2 of 2 | Archive leaderboard | report |
| Atrial Fibrillation Detection | PhysioNet Challenge 2017 | MINA | ROC-AUC | 0.9488 | #2 of 2 | Archive leaderboard | report |
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Methods
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