Knowledge-Driven Graph Representation Learning for Myocardial Infarction Localization

• Published in: IEEE journal of biomedical and health informatics Vol. 29; no. 9; pp. 6637 - 6650
• Main Authors: Guo, Fengyi, An, Ying, Kuang, Hulin, Wang, Jianxin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2025
• Subjects: Algorithms; Data models; Databases, Factual; Deep Learning; electrocardiogram (ECG); Electrocardiography; Electrocardiography - methods; Feature extraction; graph representation learning; Humans; imbalanced data; knowledge graph (KG); Knowledge graphs; Lead; Location awareness; Medical diagnostic imaging; Myocardial infarction; Myocardial Infarction - diagnosis; Myocardial Infarction - diagnostic imaging; Myocardial Infarction - physiopathology; Myocardium; Representation learning; Signal Processing, Computer-Assisted
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3574688

The electrocardiogram (ECG) serves as a crucial tool for myocardial infarction (MI) localization, and deep learning methods have proven effective in assisting physicians with MI localization. Traditional MI localization methods are purely data-driven, and the quality of the data significantly affects the model's performance, particularly in the localization of rare MI. We propose a knowledge-driven graph representation learning (KD-GRL) framework which is designed to guide deep learning models in identifying key features for MI localization using prior knowledge. The MI localization knowledge graph (KG) is constructed by integrating medical knowledge about MI localization, including ECG leads and morphological manifestations, the correlations between MI localization labels, diagnostic rules, and patient demographic information. KG effectively represents the relationships among various entities, which include ECG signal entities, morphological feature entities, and demographic feature entities. The embeddings of these entities are obtained using parallel patient multi-feature extractors. Additionally, a KG aggregation method based on edge relation projection (ERP) is proposed to aggregate the relational information in the MI localization KG. Ultimately, the MI localization task is transformed into a link prediction task between patient entity and localization label entities within the KG. We conduct experiments on two public datasets, PTB and PTBXL, achieving F1-scores of 48.90% and 46.06%, respectively, both surpassing the comparison methods. Additionally, due to the incorporation of diagnostic knowledge, our method outperforms the comparison methods in localizing rare MIs.