Millimeter-Wave Radar-Based Weak Neonatal Heart Rate Detection Using an Adaptive Subband Variable Step-Size LMS Filtering Algorithm

Non-contact measurement plays a crucial role in monitoring the heart rate of preterm and low birth weight infants in the neonatal intensive care unit (NICU). Addressing the challenges of weak heartbeat signals easily overwhelmed by noise in non-contact heart rate detection for these neonates, this paper proposes a millimeter-wave radar-based heart rate detection method using adaptive subband variable step-size least mean square (LMS) filtering. The innovative approach divides the chest echo signal into multiple subbands, employing an error-based variable step-size update strategy in each subband. By utilizing the abdominal signal as a reference, the heartbeat information is enhanced through adaptive filtering, and the results from various subbands are fused. The heart rate estimation is achieved by combining the fused results with time-frequency analysis using wavelet transform. Experimental results on data collected from multiple preterm infants in the NICU demonstrate that the proposed algorithm can reduce the root mean square error (RMSE) of preterm infant heart rate estimation to below 5 Beats Per Minute (BPM), providing a novel solution for the application of millimeter-wave radar in NICU heart rate monitoring.