LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radar
This work addresses unobtrusive cardiac monitoring for healthcare applications, presenting a novel method with interpretable features, though it is incremental in advancing radar-based ECG reconstruction.
The paper tackled the problem of non-contact ECG reconstruction from radar signals by proposing LifWavNet, a lifting wavelet network that adaptively captures features and synthesizes ECG waveforms, achieving state-of-the-art performance on two public datasets with improved reconstruction and downstream vital sign estimation.
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.