SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg.
Department of General Pediatrics and Neonatology, Saarland University Medical School, Homburg, Germany.
Sci Rep. 2022 Mar 25;12(1):5150. doi: 10.1038/s41598-022-08836-3.
Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital parameters and sensors need to be directly attached to their fragile skin. Besides mobility restrictions and stress, these sensors often cause skin irritation and may lead to pressure necrosis. In this work, we show that a contactless radar-based approach is viable for breathing monitoring in the Neonatal intensive care unit (NICU). For the first time, different scenarios common to the NICU daily routine are investigated, and the challenges of monitoring in a real clinical setup are addressed through different contributions in the signal processing framework. Rather than just discarding measurements under strong interference, we present a novel random body movement mitigation technique based on the time-frequency decomposition of the recovered signal. In addition, we propose a simple and accurate frequency estimator which explores the harmonic structure of the breathing signal. As a result, the proposed radar-based solution is able to provide reliable breathing frequency estimation, which is close to the reference cabled device values most of the time. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a completely contactless solution for vital signs monitoring.
生命体征监测系统在住院新生儿的护理中至关重要。由于早产儿器官和免疫系统尚未成熟,因此需要持续监测其生命参数,并且传感器需要直接贴附在他们脆弱的皮肤上。除了活动受限和压力之外,这些传感器经常会引起皮肤刺激,并且可能导致压迫性坏死。在这项工作中,我们展示了一种基于非接触式雷达的方法,该方法可用于新生儿重症监护病房(NICU)中的呼吸监测。这是首次研究了 NICU 日常中常见的不同场景,并通过信号处理框架中的不同贡献来解决实际临床设置中的监测挑战。我们提出了一种新颖的基于恢复信号时频分解的随机身体运动缓解技术,而不是仅仅丢弃强干扰下的测量值。此外,我们还提出了一种简单而准确的频率估计器,该估计器探索了呼吸信号的谐波结构。因此,所提出的基于雷达的解决方案能够提供可靠的呼吸频率估计,该估计值在大多数情况下都接近参考有线设备的值。我们的研究结果揭示了这项技术的优缺点,并为未来朝着完全非接触式生命体征监测解决方案的研究奠定了基础。