mlsanityData to 7 Oct 2026

MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals

arXiv 1905.11333 ↗First posted 27 May 2019eess.SP cs.LGcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2019.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 2; 2025: 5; 2026: 4.

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  1. 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
  2. 2025Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Modelcited by 36
  3. 2021Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signalscited by 35
  4. 2021PyHealth: A Python Library for Health Predictive Modelscited by 9
  5. 2021SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Modelscited by 9
  6. 2022Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networkscited by 8
  7. 2023Physics Inspired Hybrid Attention for SAR Target Recognitioncited by 6
  8. 2023MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferringcited by 5
  9. 2024MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Networkcited by 5
  10. 2023PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modelingcited by 4
  11. 2025UniECG: Understanding and Generating ECG in One Unified Modelcited by 4
  12. 2022IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classificationcited by 3
9 more
  1. 2022MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGscited by 2
  2. 2024Deep Latent Variable Modeling of Physiological Signalscited by 0
  3. 2025Hierarchical Attention Network for Interpretable ECG-based Heart Disease Classificationcited by 0
  4. 2025Manifold-Aware Diffusion-Augmented Contrastive Learning for Noise-Robust Biosignal Representationcited by 0
  5. 2025EfficientECG: Cross-Attention with Feature Fusion for Efficient Electrocardiogram Classificationcited by 0
  6. 2026BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Diagnosiscited by 0
  7. 2026MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detectioncited by 0
  8. 2026Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populationscited by 0
  9. 2026Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistencycited by 0