Guo Lin, Zhan Qianyun, Yang Jichao, An Ying, Long Jun, Ma Nan
Big Data Institute, Central South University, Changsha, 410083, China.
School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Methods. 2025 Feb;234:315-323. doi: 10.1016/j.ymeth.2025.01.015. Epub 2025 Jan 21.
The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to localize myocardial infarction (MI) that is characterized by abnormal waveform patterns on the ECG. MI is a serious cardiovascular disease, and accurate, timely diagnosis is crucial for preventing severe outcomes. Current ECG analysis methods mainly rely on intra- and inter-lead feature extraction, but most models overlook the medical knowledge relevant to disease diagnosis. Moreover, existing models often fail to effectively utilize the global spatial relationships within multi-lead ECGs, limiting their ability to comprehensively understand and accurately localize the complex pathological mechanisms of MI. To address these issues, we propose a knowledge-driven overlapping lead grouping method. Based on clinical diagnostic knowledge, we group the 12 leads according to their relevance to MI localization while retaining the full set of 12 leads as a unified group. Additionally, we design a multi-stage learning network that first extracts basic features through initial convolutional layer and progressive convolutional block, followed by SE-enhanced multi-scale residual block and positional Transformer block to gradually learn deeper intra- and inter-lead features. Furthermore, we propose a branch-level weighted feature integration mechanism to effectively fuse the features extracted from each group. The proposed method was thoroughly evaluated on the publicly available multi-label PTB-XL dataset and achieved over 80% prediction accuracy for MI localization tasks. The results demonstrated significant improvements across several metrics compared to current state-of-the-art methods, confirming its exceptional performance.
心电图（ECG）是一种广泛使用的医学诊断工具，用于定位心肌梗死（MI），其特征是心电图上出现异常波形模式。心肌梗死是一种严重的心血管疾病，准确、及时的诊断对于预防严重后果至关重要。当前的心电图分析方法主要依赖导联内和导联间的特征提取，但大多数模型忽略了与疾病诊断相关的医学知识。此外，现有模型往往无法有效利用多导联心电图中的全局空间关系，限制了它们全面理解和准确定位心肌梗死复杂病理机制的能力。为了解决这些问题，我们提出了一种知识驱动的重叠导联分组方法。基于临床诊断知识，我们根据导联与心肌梗死定位的相关性对12导联进行分组，同时将完整的12导联作为一个统一的组保留。此外，我们设计了一个多阶段学习网络，该网络首先通过初始卷积层和渐进卷积块提取基本特征，然后是SE增强的多尺度残差块和位置Transformer块，以逐步学习更深层次的导联内和导联间特征。此外，我们提出了一种分支级加权特征集成机制，以有效地融合从每个组中提取的特征。我们在公开可用的多标签PTB-XL数据集上对所提出的方法进行了全面评估，在心肌梗死定位任务中实现了超过80%的预测准确率。结果表明，与当前的最先进方法相比，该方法在多个指标上有显著改进，证实了其卓越的性能。
