Szmola Benedek, Hornig Lars, Wolf Karen Insa, Radeloff Andreas, Witt Karsten, Kollmeier Birger
Department of Neurology, School of Medicine and Health Science, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany.
Medizinische Physik, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany.
Sensors (Basel). 2025 Apr 20;25(8):2596. doi: 10.3390/s25082596.
Radars are promising tools for contactless vital sign monitoring. As a screening device, radars could supplement polysomnography, the gold standard in sleep medicine. When the radar is placed lateral to the person, vital signs can be extracted simultaneously from multiple body parts. Here, we present a method to select every available breathing and heartbeat signal, instead of selecting only one optimal signal. Using multiple concurrent signals can enhance vital rate robustness and accuracy. We built an algorithm based on persistence diagrams, a modern tool for time series analysis from the field of topological data analysis. Multiple criteria were evaluated on the persistence diagrams to detect breathing and heartbeat signals. We tested the feasibility of the method on simultaneous overnight radar and polysomnography recordings from six healthy participants. Compared against single bin selection, multiple selection lead to improved accuracy for both breathing (mean absolute error: 0.29 vs. 0.20 breaths per minute) and heart rate (mean absolute error: 1.97 vs. 0.66 beats per minute). Additionally, fewer artifactual segments were selected. Furthermore, the distribution of chosen vital signs along the body aligned with basic physiological assumptions. In conclusion, contactless vital sign monitoring could benefit from the improved accuracy achieved by multiple selection. The distribution of vital signs along the body could provide additional information for sleep monitoring.
雷达是用于非接触式生命体征监测的有前景的工具。作为一种筛查设备,雷达可以补充睡眠医学的金标准——多导睡眠图。当雷达放置在人体侧面时,可以从多个身体部位同时提取生命体征。在此,我们提出一种方法,即选择每一个可用的呼吸和心跳信号,而不是只选择一个最优信号。使用多个并发信号可以提高生命体征率的稳健性和准确性。我们基于持久图构建了一种算法,持久图是拓扑数据分析领域中用于时间序列分析的现代工具。在持久图上评估多个标准以检测呼吸和心跳信号。我们在来自六名健康参与者的夜间雷达和多导睡眠图同步记录上测试了该方法的可行性。与单仓选择相比,多信号选择在呼吸(平均绝对误差:每分钟0.29次呼吸对0.20次呼吸)和心率(平均绝对误差:每分钟1.97次心跳对0.66次心跳)方面都提高了准确性。此外,选择的伪信号段更少。此外,沿身体选定的生命体征分布符合基本的生理假设。总之,非接触式生命体征监测可以受益于多信号选择所实现的更高准确性。沿身体的生命体征分布可为睡眠监测提供额外信息。