用单导联数据重建六导联心电图，轻量且可解释。
ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring
- 基于凸优化重构两层ReLU网络，实现可解释的轻量推理
- 25名患者数据验证，精度接近大模型但计算开销显著降低
- 适合嵌入式设备实时心电监测，尤其关注可解释性场景
我们提出ConvexECG，一种用于从单导联数据重建六导联心电图（ECG）的可解释且资源高效的方案，旨在推动个性化连续心脏监测。该方法通过将两层ReLU神经网络转化为凸形式，实现高效训练与部署，并具备确定性和可解释性。基于25名患者的实验数据表明，ConvexECG在保持与大型神经网络相当精度的同时，显著降低了计算开销，展现出在实时、低资源环境下的应用潜力。
原文摘要 · Abstract (English)
We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for efficient training and deployment in resource constrained environments, while also having deterministic and explainable behavior. Using data from 25 patients, we demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead, highlighting its potential for real-time, low-resource monitoring applications.
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