ConvexECG：用于个性化、连续心脏监测的轻量级可解释神经网络
ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring
AI总结：
ConvexECG通过凸重构两层ReLU网络，从单导联重建六导联心电图，在25名患者数据上达到与大型网络相当的精度且计算开销更低，适用于实时低资源监测。
摘要：
我们提出了ConvexECG，一种可解释且资源高效的方法，用于从单导联数据重建六导联心电图（ECG），旨在推进个性化和连续的心脏监测。ConvexECG利用两层ReLU神经网络的凸重构，使其能够在资源受限环境中高效训练和部署，同时具有确定性和可解释的行为。使用25名患者的数据，我们证明ConvexECG在显著降低计算开销的同时，达到了与更大神经网络相当的准确性，突显了其在实时、低资源监测应用中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
