Reconstructing ECG-Like Signals From Millimeter-Wave Radar Echoes: A Generative Adversarial Network Approach

Electrode-based electrocardiogram (ECG) measurement devices often lead to user discomfort and raise privacy concerns. In contrast, millimeter-wave radar offers a non-invasive alternative by enabling real-time heartbeat monitoring without direct physical contact. Nevertheless, radar outputs differ significantly from traditional ECG signals, complicating medical diagnosis. To address this challenge, we propose a method for accurately reconstructing ECG-like signals from millimeter-wave radar data using deep convolutional generative adversarial networks enhanced with an attention mechanism (Attention-DCGAN). Based on the MMECG public dataset, we first apply an improved K-means algorithm to cluster heartbeat data from various heart locations. We then train the Attention-DCGAN, consisting of a generator with a 4-layer deconvolution module and a 1-layer attention module, alongside a discriminator with a 4-layer convolution module, to convert radar-derived heartbeat data into ECG-like signals. Experimental results demonstrate that our method achieves higher reconstruction accuracy than traditional methods, with a root-mean-square error of 0.0329 mV and a Pearson correlation coefficient of 0.9853.