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 non-contact, 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 μVpp 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 UNet-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 7.04M parameters, Floating-Point Operations (FLOPs) of 889.16 M, real-time processing speed of 1.05 s per execution, and low power consumption of 379.5mW. Moreover, it requires just 29.13MB of Random Access Memory (RAM) and 10.49MB of Read-Only Memory (ROM), making it highly suitable for deployment in embedded systems. Experimental results from both a 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 non-contact healthcare and medical applications.