用多任务学习提升雷达心电图重建的抗干扰能力
radarODE-MTL: A Multi-Task Learning Framework with Eccentric Gradient Alignment for Robust Radar-Based ECG Reconstruction
- 将雷达心电重建拆解为三个任务，联合优化提升鲁棒性
- 在含噪环境下仍保持高精度，准确率显著优于基线方法
- 适合医疗监测、可穿戴设备等真实场景应用
毫米波雷达有望以无感方式实现可靠的生理信号监测。然而，环境噪声或身体随机运动会导致雷达信号畸变，破坏微弱的心脏活动，影响生命体征恢复。特别是基于深度学习的心电图（ECG）重建对噪声敏感。本文创新性地将雷达基ECG恢复分解为三个独立任务，提出雷达ODE-MTL多任务学习框架，增强对持续性和突发性噪声的鲁棒性。为进一步缓解任务间优化冲突，提出新型多任务优化策略——偏心梯度对齐（EGA），通过正交空间中动态裁剪任务特异性梯度来适应任务难度。在公开数据集上的实验表明，雷达ODE-MTL结合EGA在噪声环境下性能稳定且准确率显著提升，能从雷达信号中鲁棒地重构精确心电信号，具备实际应用潜力。代码已开源。
原文摘要 · Abstract (English)
Millimeter-wave radar is promising to provide robust and accurate vital sign monitoring in an unobtrusive manner. However, the radar signal might be distorted in propagation by ambient noise or random body movement, ruining the subtle cardiac activities and destroying the vital sign recovery. In particular, the recovery of electrocardiogram (ECG) signal heavily relies on the deep-learning model and is sensitive to noise. Therefore, this work creatively deconstructs the radar-based ECG recovery into three individual tasks and proposes a multi-task learning (MTL) framework, radarODE-MTL, to increase the robustness against consistent and abrupt noises. In addition, to alleviate the potential conflicts in optimizing individual tasks, a novel multi-task optimization strategy, eccentric gradient alignment (EGA), is proposed to dynamically trim the task-specific gradients based on task difficulties in orthogonal space. The proposed radarODE-MTL with EGA is evaluated on the public dataset with prominent improvements in accuracy, and the performance remains consistent under noises. The experimental results indicate that radarODE-MTL could reconstruct accurate ECG signals robustly from radar signals and imply the application prospect in real-life situations. The code is available at: http://github.com/ZYY0844/radarODE-MTL.
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