Abstract
Electrocardiogram (ECG), as a crucial find-grained cardiac feature, has been successfully recovered from radar signals in the literature, but the performance heavily relies on the high-quality radar signal and numerous radar-ECG pairs for training, restricting the applications in new scenarios due to data scarcity. Therefore, this work will focus on radar-based ECG recovery in new scenarios with limited data and propose a cardio-focusing and -tracking (CFT) algorithm to precisely track the cardiac location to ensure an efficient acquisition of high-quality radar signals. Furthermore, a transfer learning model (RFcardi) is proposed to extract cardio-related information from the radar signal without ECG ground truth based on the intrinsic sparsity of cardiac features, and only a few synchronous radar-ECG pairs are required to fine-tune the pre-trained model for the ECG recovery. The experimental results reveal that the proposed CFT can dynamically identify the cardiac location, and the RFcardi model can effectively generate faithful ECG recoveries after using a small number of radar-ECG pairs for training. The code and dataset are available after the publication.
Abstract (translated)
心电图（ECG）作为重要的心脏特征之一，在文献中已经成功地从雷达信号中恢复出来，但其性能严重依赖于高质量的雷达信号和大量的雷达-ECG配对数据进行训练。由于新场景下数据稀缺，限制了应用范围。因此，本工作将专注于在数据有限的新应用场景中的基于雷达的心电图恢复，并提出了一种心脏聚焦与跟踪（Cardio-Focusing and -Tracking, CFT）算法，以精准地追踪心脏位置，确保高效获取高质量的雷达信号。 此外，我们还提出了一个迁移学习模型（RFcardi），该模型可以在没有心电图真实值的情况下从雷达信号中提取心脏相关的信息，并利用心脏特征内在的稀疏性进行操作。仅需少量同步的雷达-ECG配对数据即可微调预训练模型以实现心电图恢复。 实验结果表明，所提出的CFT算法能够动态识别心脏位置，而RFcardi模型能够在使用少量雷达-ECG配对数据训练后有效生成准确的心电图恢复。发布之后代码和数据集将对外提供。
URL
https://arxiv.org/abs/2506.19358
