计算机技术与发展2026，Vol.36Issue(6)：156-164,9.DOI:10.20165/j.cnki.ISSN1673-629X.2026.0001
基于语义引导与多头交叉融合的心梗检测模型
A Myocardial Infarction Detection Model Based on Semantic Guidance and Multi-head Cross-attention Fusion
摘要
Abstract
Timely detection of Myocardial Infarction(MI)is crucial for reducing mortality rates and informing clinical decision-making.Existing deep learning-based MI detection methods have begun to adopt multimodal approaches to enhance performance.However,some current methods that concatenate ECG waveforms and diagnostic texts may amplify semantic noise and fail to adaptively balance modal contributions.Meanwhile,certain methods applying global alignment paradigms in the cardiovascular domain to obtain general representations struggle to capture the specific aligned representations between MI ECG waveforms and textual semantics,making it difficult to effectively distinguish between ST-segment elevation myocardial infarction(STEMI)and its mimics—such as angina,pericarditis,and hyperkalemia—particularly in the presence of textual noise,while maintaining interpretability.To address these issues,we propose a myocardial infarction detection model named SCAFDM,based on semantic guidance and multi-head cross-attention fusion.The model designs a multi-head cross-attention mechanism to align ECG waveforms and diagnostic texts,dynamically assigning weights to text tokens to suppress semantic noise and achieve adaptive inter-modal balance.Additionally,an end-to-end joint loss training method is adopted to resolve the feature alignment problem in multimodal MI detection.Experiments on the PTB-XL dataset demonstrate that SCAFDM achieves excellent performance in terms of area under the ROC curve and F1 score.关键词
心肌梗死/12导联心电图/诊断文本/语义引导/多模态融合/联合损失/多头交叉注意力机制Key words
myocardial infarction/12-lead electrocardiogram(ECG)/diagnostic text/semantic guidance/multimodal fusion/joint loss/multi-head cross-attention mechanism分类
信息技术与安全科学引用本文复制引用
童乐,杨湘,邱晨..基于语义引导与多头交叉融合的心梗检测模型[J].计算机技术与发展,2026,36(6):156-164,9.基金项目
国家自然科学基金委员会,国家青年科学基金项目(62507036) （62507036）
湖北省教育厅科学技术研究计划重点项目(D20231104) （D20231104）
