Respiration and heartbeat are important vital signs of the human body. More detection systems, for these vital signs, especially for noncontact measurements, are being developed. However, owing to the large amount of noise in the signals, the accurate separation of respiratory and heartbeat signals has become challenging in the field of vital sign detection. We propose an optimized variational mode decomposition (VMD) method to realize the accurate separation of respiratory and heartbeat signals. We apply the relative entropy and sample entropy to optimize the VMD (RESE-VMD) algorithm and introduce the RESE-VMD method into 77-GHz millimeter-wave radar signal processing. The ideal parameters of the variational model are searched by relative entropy, and each modal function and central frequency are continuously updated to effectively avoid modal confounding. The signal is reconstructed by selecting the appropriate signal components according to the sample entropy and correlation coefficient. Accordingly, we build real experimental scenarios for the experiments; the results show that our method achieves better performance in separating respiratory and heartbeat signals. Our results pave the way for important applications in clinical noncontact vital sign detection.
