论文标题
新生儿重症监护病房的基于深度学习的非接触性生理监测
Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit
论文作者
论文摘要
新生儿重症监护病房（NICU）中的早产婴儿必须持续监测其心脏健康。常规的监测方法基于接触，使新生儿容易受到各种医院感染。基于视频的监控方法为非接触式测量开辟了潜在的途径。这项工作提供了一条管道，用于远程对NICU设置视频的心肺信号进行远程估算。我们提出了一个端到端的深度学习（DL）模型，该模型整合了一种基于非学习的方法来产生替代地面真理（SGT）标签以进行监督，从而避免了直接依赖对真实地面真相标签的直接依赖。我们进行了扩展的定性和定量分析，以检查我们提出的基于DL的管道的疗效，并在估计的心率中达到了总平均平均绝对误差为4.6 BEATS（BPM）（BPM）（BPM）和均方根均为6.2 bpm。
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.
