发表机构
Tokyo University of Agriculture and Technology（东京农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TinyCardioUNet利用图神经网络编码IMU轴间依赖，并通过张量分解缩减参数，以轻量级UNet实现从IMU到ECG的准确重建，在公共数据集上以36.0k参数达到0.098的RMSE和0.677的相关系数。
AI 中文摘要
从胸戴式惯性测量单元（IMU）估算心电图（ECG）可实现无需电极不适感的连续心率（HR）监测。我们提出TinyCardioUNet，一种轻量级UNet，它使用全部六个IMU轴而无需事先进行通道选择，通过图神经网络细化其瓶颈部分以编码轴间依赖，并采用具有自动变分贝叶斯秩选择的张量分解进行参数缩减。在公共数据集上，TinyCardioUNet以仅36.0k参数实现了0.098的均方根误差（RMSE）和0.677的皮尔逊相关系数，并且对加性噪声保持相对稳健，展示了紧凑模型下的准确ECG重建。
英文摘要
Estimating electrocardiography (ECG) from a chest-worn inertial measurement unit (IMU) enables continuous heart rate (HR) monitoring without the discomfort of electrodes. We propose TinyCardioUNet, a lightweight UNet that uses all six IMU axes without prior channel selection, refines its bottleneck with a graph neural network that encodes inter-axis dependencies, and employs tensor decomposition with automatic variational Bayesian rank selection for parameter reduction. On a public dataset, TinyCardioUNet achieves an RMSE of $0.098$ and a Pearson correlation coefficient of $0.677$ with only $36.0$k parameters and remains comparatively robust to additive noise, demonstrating accurate ECG reconstruction with a compact model.
CommentsThe source code and pretrained models are available at https://github.com/ttlabtuat/TinyCardioUNet
