arXiv：cs.LG· Shreyasvi Natraj, Cyrus Achtari, Felice Gragnano, Andrea Milzi, Marco Valgimigli, Diego Paez-Granados·· 4 小时前AI 评分43
ECGLight：面向纸质 ECG 数字化与心肌梗死筛查的轻量计算框架
ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening
AI 导读
ECGLight 是一套端到端轻量级端侧流水线，可将纸质 ECG 的手机照片或扫描件转为校准后的 12 导联信号，并筛查心肌梗死（MI）病变，同时用 SHAP 提供可解释性。
正文
Abstract: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. Github: this https URL
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.07683 [cs.LG] |
| (or arXiv:2607.07683v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.07683 arXiv-issued DOI via DataCite |
Submission history
From: Shreyasvi Natraj [view email]
[v1]
Wed, 8 Jul 2026 17:42:00 UTC (43,179 KB)
[v2]
Wed, 7 Oct 2026 12:55:58 UTC (43,179 KB)
来源：arXiv：cs.LG · arxiv.org