用手机拍照即可自动识别心电图是否异常，适合偏远地区医疗使用
ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening
- 轻量级端侧框架，仅靠手机CPU在30秒内完成纸版心电图数字化与诊断
- 在PTB-XL数据集上对心肌梗死检测准确率达95.51%，在医院数据集上对前壁梗死检测达88.89%
- 支持可解释性分析，适合资源匮乏地区的基层医疗和移动筛查
心电图是诊断心血管疾病最常用的方法之一，但许多偏远诊所仍依赖纸质打印结果，受限于网络与算力，无法接入基于人工智能的辅助诊断系统，导致急性冠脉闭塞等危急情况被遗漏。现有研究多将数字化与诊断分开处理，且依赖复杂模型。本文提出一个端到端的轻量化框架，仅需手机拍摄纸版心电图，即可生成校准的12导联信号，并筛查心肌梗死（MI）病征。系统在21,799例来自PTB-XL的数据上训练并验证，进一步在医院采集的ECG-Matrix数据集上测试，全程运行时间低于30秒（仅用CPU），在PTB-XL上对心肌梗死检测准确率95.51%（F1=0.9519），在ECG-Matrix上对前壁心肌梗死检测准确率88.89%（F1=0.8862）。通过SHAP提供可解释性支持。本工作证明了传统纸质记录可在全球任何地方可靠数字化，为缺乏数字导出、网络或高端算力的场景提供可扩展的决策支持。
原文摘要 · Abstract (English)
Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysis due to limited connectivity and computational capacity. As a result, vast numbers of physical ECGs obtained in remote areas still remain incapable of being accessed by contemporary artificial-intelligence (AI)-based decision support as they require high computational resources or strong high-speed internet connectivity. This causes several cases where conditions like acute coronary occlusion (ACS) is overlooked and reperfusion therapy delayed. Although prior work has tackled digitization and diagnosis separately, and utilized advanced AI models for them, there still remains a lack of a compute-light, on-device framework that reconstructs paper ECGs at high fidelity, while accurately supporting multiple clinically relevant endpoints. We address this need with an end-to-end lightweight on-device digitization-to-diagnosis pipeline that converts a smartphone photo or scan of a paper ECG into a calibrated 12-lead signal and screens for Myocardial Infarction (MI) pathologies, with SHapley Additive exPlanations (SHAP) to support interpretability. Trained and evaluated on 21,799 ECGs from the PTB-XL dataset and further validated on hospital-acquired ECG-Matrix dataset, the complete system runs in <30 s per ECG on CPU-only resources, achieving 95.51% accuracy (F1 = 0.9519) for MI detection on PTB-XL and 88.89% accuracy (F1 = 0.8862) for OMI detection on ECG-Matrix. This work showcases that legacy paper records can be reliably democratized in any part of the world, providing a scalable decision support when digital ECG export, connectivity, or high-end compute are unavailable
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