{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mina-multilevel-knowledge-guided-attention","title":"MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals","arxiv_id":"1905.11333","date":"2019-05-27","proceeding":null,"authors":["Shenda Hong","Cao Xiao","Tengfei Ma","Hongyan Li","Jimeng Sun"],"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.","url_abs":"https://arxiv.org/abs/1905.11333v3","url_pdf":"https://arxiv.org/pdf/1905.11333v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mina-multilevel-knowledge-guided-attention","repo_url":"https://github.com/hsd1503/MINA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"rhythm","task_name":"Rhythm"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/atrial-fibrillation-detection-on-physionet","task":"Atrial Fibrillation Detection","dataset":"PhysioNet Challenge 2017","model":"MINA","rank_in_archive_order":2,"of":2,"metrics":{"F1":"0.8342","PR-AUC":"0.9436","ROC-AUC":"0.9488"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1905.11333","atlas_url":"https://app.syntology.ai/?focus=1905.11333","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}