An Onboard Executable Multitask Network Model for Bioradar-Based ECG Signal Reconstruction Using High-Fidelity DHD Signals

Bioradar-based electrocardiogram (ECG) reconstruction shows great promise in replacing traditional contact-based ECG collection methods for noncontact, long-term healthcare applications, such as continuous cardiorespiratory disease monitoring and early warning systems. In this work, we first designed a practical bioradar system for high signal-to-noise ratio (SNR) I/Q baseband signal acquisition, achieving excellent performance with background noise not exceeding 3.18 \mu Vpp and SNRs ranging from 26 to 119 dB. Next, we developed a high-linearity arctangent demodulation method to extract high-fidelity Doppler heartbeat diagram (DHD) signals. Finally, we introduced a lightweight U-Net-based multitask network model for ECG signal reconstruction, which demonstrated performance achieving root mean squared error (RMSE) values of 0.160 and 0.330, root mean absolute error (RMAE) values of 0.261 and 0.400, and Pearson correlation coefficient (PCC) values of 95.17% and 85.26% for two different datasets, respectively. This model is characterized by low computational complexity, with 7.04 M parameters, floating-point operations (FLOPs) of 889.16 M, real-time processing speed of 1.05 s/execution, and low power consumption of 379.5 mW. Moreover, it requires just 29.13 MB of random access memory (RAM) and 10.49 MB of read-only memory (ROM), making it highly suitable for deployment in embedded systems. Experimental results from both public dataset and our own dataset show that the proposed lightweight ECG reconstruction model, when combined with the designed high-fidelity DHD signal acquisition bioradar system, holds significant potential for noncontact healthcare and medical applications.