用少量数据实现雷达信号到心电图的高精度转换
From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data
- 通过聚焦心脏位置动态优化雷达信号采集
- 仅需少量配对数据即可完成心电图重建
- 适合医疗监测等数据稀缺场景
心电图（ECG）作为关键的心脏精细特征，已有研究从雷达信号中成功恢复，但性能高度依赖高质量雷达信号和大量同步雷达-ECG数据对，限制了在新场景中的应用。本文针对数据稀缺场景，提出一种心脏聚焦与追踪（CFT）算法，可精准定位心脏位置，确保高质量雷达信号的高效获取。同时提出一种迁移学习模型RFcardi，基于心脏特征的内在稀疏性，无需真实心电图标签即可从雷达信号中提取心相关信息，仅需少量同步雷达-ECG对即可微调模型实现心电图恢复。实验表明，所提CFT能动态识别心脏位置，RFcardi在少量训练数据下仍能生成高保真心电图。代码与数据集将在发表后公开。
原文摘要 · Abstract (English)
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.
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