用可学习小波网络，从雷达信号还原出无接触心电图。
LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radar
- 基于可学习小波的多分辨率分析与合成框架，自适应提取雷达特征。
- 在两个公开数据集上，心电图重建和心率变异性估计均优于现有方法。
- 小波分解过程具可解释性，适合医疗监测与智能健康场景。
从雷达信号非接触式重构心电图（ECG）为无创心脏监测提供了新途径。我们提出LifWavNet，一种基于多分辨率分析与合成（MRAS）模型的可学习提升小波网络，用于雷达到心电图的转换。与以往使用固定小波的方法不同，LifWavNet采用可学习的提升小波，通过提升与逆提升单元自适应捕捉雷达信号特征并合成具有生理意义的心电波形。为提高重建保真度，引入多分辨率短时傅里叶变换（STFT）损失，确保在时域和频域上与真实心电图保持一致。在两个公开数据集上的评估表明，LifWavNet在心电图重建及下游生命体征估计（心率与心率变异性）方面均优于当前最先进方法。此外，中间特征可视化揭示了多分辨率分解与合成过程的可解释性。这些结果确立了LifWavNet作为雷达基非接触式心电测量的稳健框架。
原文摘要 · Abstract (English)
Non-contact electrocardiogram (ECG) reconstruction from radar signals offers a promising approach for unobtrusive cardiac monitoring. We present LifWavNet, a lifting wavelet network based on a multi-resolution analysis and synthesis (MRAS) model for radar-to-ECG reconstruction. Unlike prior models that use fixed wavelet approaches, LifWavNet employs learnable lifting wavelets with lifting and inverse lifting units to adaptively capture radar signal features and synthesize physiologically meaningful ECG waveforms. To improve reconstruction fidelity, we introduce a multi-resolution short-time Fourier transform (STFT) loss, that enforces consistency with the ground-truth ECG in both temporal and spectral domains. Evaluations on two public datasets demonstrate that LifWavNet outperforms state-of-the-art methods in ECG reconstruction and downstream vital sign estimation (heart rate and heart rate variability). Furthermore, intermediate feature visualization highlights the interpretability of multi-resolution decomposition and synthesis in radar-to-ECG reconstruction. These results establish LifWavNet as a robust framework for radar-based non-contact ECG measurement.
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