LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radar

TL;DR

LifWavNet is a novel learnable wavelet-based neural network that enhances non-contact ECG reconstruction from radar signals, outperforming existing methods in accuracy and interpretability.

Contribution

The paper introduces LifWavNet, a learnable lifting wavelet network with multi-resolution analysis for improved radar-to-ECG reconstruction.

Findings

Outperforms state-of-the-art in ECG reconstruction accuracy

Enables better vital sign estimation from radar signals

Provides interpretable multi-resolution feature representations

Abstract

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.