SCH: EXP: RadiOptiMeter: Long-Term and Fine-Grained Breathing Volume Monitoring for Sleep Disordered Breathing (SDB)

儿童睡眠呼吸障碍（SDB）被认为是一个公共卫生问题，具有严重的后果，如认知功能下降，学习成绩差，白天嗜睡和心血管风险增加。目前SDB的诊断是在医院睡眠实验室进行的，通过在病人身上的不同位置安装一系列心肺传感器来监测病人。的身体。这种突兀的监测形式对患者来说是不方便的，并且需要技术人员的大量关注以确保研究质量。特别是儿童，他们对这项研究的耐受性非常差;经常移除传感器，睡眠困难，需要重复研究。该项目旨在开发一种新的方法，在睡眠研究期间使用射频信号辅助的光学信号来远程连续监测人类受试者的呼吸量和呼吸模式。我们介绍了RadiOptiMeter，一种混合无线电-光学呼吸量监测方法，将射频（RF）信号的独特特性与深度-CO2-热摄像机捕获的图像流相结合，以准确估计远处睡眠患者的呼吸量。这项研究是无创呼吸监测发展的第一步。利用这种新颖的非侵入性设备来测量睡眠期间的呼吸，将开始一项研究计划，以促进未来在儿童舒适的条件下诊断SDB？在医院里，没有实验室的费用，也没有一次性医疗设备的费用，相当于每年在每个睡眠实验室花费数千美元。该项目将研究一种新的方法，在医院睡眠研究期间使用射频和光学信号连续监测人类的呼吸量和呼吸模式。我们介绍了RadiOptiMeter，一种混合无线电-光学呼吸量监测方法，将射频（RF）信号的独特特性与深度-CO2-热摄像机捕获的图像流相结合，以准确，连续地估计远处睡眠患者的呼吸量。我们提出的技术，以解决在睡眠中的身体运动，环境无线信号噪声和患者人群的多样性带来的挑战。预期的结果是SDB研究的稳健和准确的呼吸量监测系统。每一个建议的设备之间的合作，使我们能够利用设备的协同效应，以涵盖每个设备类型所施加的限制，并提供系统冗余。这些冗余确保了长期监测任务的可靠性，这对临床应用至关重要。我们提出的研究将做出以下关键贡献，以实现新的非接触式生命信号监测系统：（1）分析模型，实验工具，和评估结果的呼吸量估计方法使用基于视觉的系统（VVE），其中包括4D体积模型和骨骼结构分析从深度- CO2-热（DCT）相机输出。(2)基于RF的呼吸量估计（RVE）系统的分析模型、实验硬件和软件组件以及评估结果，该系统使用基于神经网络的机器学习进行胸部位移-容积匹配。(3)一种混合式无线电-光学呼吸量估计系统（RadiOptiMeter），协同结合VVE和RVE，以执行连续和细粒度监测。RadiOptiMeter包括身体运动跟踪，自动天线转向，以及一套控制和同步算法，用于整个系统的谐波集成。计算机科学与工程系的研究人员和儿童睡眠医学研究所的医生之间的合作努力？美国科罗拉多医院是发展无创呼吸监测的第一步。一种新的非侵入性设备来测量睡眠期间的呼吸将是必要的基础研究的第一步，以促进未来的诊断SDB在舒适的儿童？在医院里，没有实验室的费用，也没有一次性医疗设备的费用，相当于每年在每个睡眠实验室花费数千美元。本计画也提供一个很好的方法来训练研究生进行这个以视觉为基础的研究计画。RadiOptiMeter概念是一个令人兴奋和吸引人的工具，用于组织各种教育活动。此外，项目成果将通过学术出版物和我们现有和潜在的工业合作伙伴的积极宣传进行传播。

Sleep disordered breathing (SDB) in children is considered to be a public health problem with serious consequences such as decreased cognitive function, poor school performance, daytime sleepiness and increased cardiovascular risk. Current diagnosis of SDB is performed in hospital sleep laboratories by monitoring patients with a host of cardiorespiratory sensors attached at various positions on the patient?s body. This obtrusive form of monitoring is inconvenient to patients and require tremendous amount of attention from technicians to ensure study quality. Children, especially, tolerate the study very poorly; often removing sensors, having trouble sleeping, and necessitating repeat investigations. This project aims to develop a new method to remotely and continuously monitor breathing volume and breathing patterns of human subjects during sleep studies using optical signals assisted by radio frequency signals. We introduce RadiOptiMeter, a hybrid radio-optical breath volume monitoring approach that couple the unique characteristics of radio frequency (RF) signals with image stream captured by a depth-CO2-thermal camera to accurately estimate breathing volume of sleeping patients from afar. This study is the first step in the development of non-invasive respiratory monitoring. Utilizing this novel and non-invasive device to measure breathing during sleep will begin a research program to promote future diagnosis of SDB in the comfort of the child?s home, with no in-hospital laboratory expenses or the expense of the disposable medical equipment which equates to thousands of dollars a year in each sleep laboratory. This project will investigate a new method to continuously monitor breathing volume and breathing patterns of humans during in-hospital sleep studies using radio frequency and optical signals. We introduce RadiOptiMeter, a hybrid radio-optical breath volume monitoring approach that couple the unique characteristics of radio frequency (RF) signals with image stream captured by a depth-CO2-thermal camera to accurately and continuously estimate breathing volume of sleeping patients from afar. We propose techniques to address challenges brought about by the body movement during sleep, environmental wireless signal noises, and the diversity of patient populations. An expected outcome is a robust and accurate breathing volume monitoring system for SDB studies. The cooperation between each of the proposed devices allows us to exploit the synergistic effects of the devices to cover the limitations imposed by each device type and provides system redundancies. These redundancies ensure reliability for long-term monitoring tasks, which are critical for clinical applications. Our proposed research will make the following key contributions to enable new non-contact vital signal monitoring system: (1) Analytical models, experimental tools, and evaluation results of a breathing volume estimation method using a vision-based system (VVE) which include 4D volumetric model and skeletal structure analysis from depth- CO2-thermal (DCT) camera outputs. (2) Analytical models, experimental hardware and software components, and evaluation results of a RF-based breathing volume estimation (RVE) system, that uses neural-network-based machine learning for chest displacement- to-volume matching. (3) A hybrid radio-optical breathing volume estimation system (RadiOptiMeter) that synergistically combines the VVE and RVE to perform continuous and fine-grain monitoring. RadiOptiMeter includes body movement tracking, automatic antenna steering, and a set of controlling and synchronizing algorithms for a harmonic integration of the whole system. This collaborative effort between researchers at the Department of Computer Science and Engineering and medical doctors at Sleep Medicine Research at the Children?s Hospital Colorado is the first step in the development of non-invasive respiratory monitoring. A novel non-invasive device to measure breathing during sleep will be the first step in the necessary foundational research to promote future diagnosis of SDB in the comfort of the child?s home, with no in-hospital laboratory expenses or the expense of the disposable medical equipment which equates to thousands of dollars a year in each sleep laboratory. This project also provides an excellent methodd to train graduate students to conduct this vision-based research project. The RadiOptiMeter concepts serves as an exciting and appealing tool for structuring a variety of educational activities. Moreover, the project results will be disseminated through scholarly publications and active outreach through our existing and potential industrial partners.