In response to the pressing requirement for prompt and precise heart rate acquisition during neonatal resuscitation, an adaptive motion artifact filter (AMF) is proposed in this study, which is based on the continuous wavelet transform (CWT) approach and takes advantage of the gradual, time-based changes in heart rate. This method is intended to alleviate the pronounced interference induced by random body movement (RBM) on radar detection in neonates. The AMF analyzes the frequency components at different time points in the CWT results. It extracts spectral peaks from each time slice of the frequency spectrum and correlates them with neighboring peaks to identify the existing components in the signal, thereby reducing the impact of RBM and ultimately extracting the heartbeat component. The results demonstrate a reliable estimation of heart rates. In practical clinical settings, we performed measurements on multiple neonatal patients within a hospital environment. The results demonstrate that even with limited data, its accuracy in estimating the resting heart rate of newborns surpasses 97%, and during infant movement, its accuracy exceeds 96%.

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.