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

SCH：EXP：RadiOptiMeter：针对睡眠呼吸障碍 (SDB) 的长期细粒度呼吸量监测

• 批准号: 1602428
• 负责人: Tam Vu
• 金额: $ 57.5万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1602428&HistoricalAwards=false
• 关键词: SCH; EXP; RadiOptiMeter; Long; Term

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.

儿童中的睡眠失调呼吸（SDB）被认为是一个公共卫生问题，其严重后果，例如认知功能降低，学校表现不佳，白天嗜睡和心血管风险增加。当前对SDB的诊断是在医院睡眠实验室中进行的，通过监测患者身体各个位置附在各个位置的心肺传感器的患者。这种监测的这种令人难以置信的形式对患者来说是不便的，需要技术人员的大量关注才能确保学习质量。尤其是儿童，对研究的忍受很差。通常会删除传感器，无法入睡并需要重复调​​查。该项目旨在开发一种新方法，以使用射频信号辅助的光信号在睡眠研究期间远程，连续监测人类受试者的呼吸量和呼吸模式。我们介绍了一种混合射光呼吸量监测方法，将射频（RF）信号的独特特征与由深度-CO2热摄像机捕获的图像流融为一体，以准确估计来自远处的睡眠患者的呼吸量。这项研究是开发非侵入性呼吸监测的第一步。利用这种新颖和非侵入性装置来测量睡眠期间的呼吸，将开始一项研究计划，以促进儿童住所舒适的SDB诊断，没有院内实验室费用或可支配医疗设备的费用，每位睡眠实验室中每年每年每年数千美元。该项目将研究一种新的方法，以使用射频和光学信号在院内睡眠研究中连续监测人类的呼吸量和呼吸模式。我们介绍了一种杂交放射光呼吸量监测方法，将射频（RF）信号的独特特征与由深度-CO2热摄像机捕获的图像流相对，以准确且连续地估算来自AfAR的睡眠患者的呼吸量。我们提出的技术来解决睡眠期间身体运动带来的挑战，环境无线信号噪声以及患者人群的多样性。预期的结果是用于SDB研究的强大而准确的呼吸量监测系统。每个提出的设备之间的合作使我们能够利用设备的协同效应，以涵盖每种设备类型所施加的限制并提供系统冗余。这些冗余确保对长期监控任务的可靠性，这对于临床应用至关重要。我们提出的研究将为实现新的非接触性信号监测系统做出以下关键贡献：（1）使用基于视觉的系统（VVE）的呼吸量估算方法的分析模型，实验工具和评估结果，其中包括4D体积模型和来自Depth-CO2-CO2热电（DCT）摄像机输出的4D体积模型和骨架结构分析。 （2）分析模型，实验硬件和软件组件以及基于RF的呼吸量估计（RVE）系统的评估结果，该系统使用基于神经网络的机器学习来进行胸部位移 - 到量匹配。 （3）一种混合射光呼吸量估计系统（放射性呼吸器），该系统协同结合了VVE和RVE，以执行连续且细粒度的监测。放射性次点包括身体运动跟踪，自动天线转向以及一组控制和同步算法，以谐波整体整体的谐波集成。计算机科学和工程学系研究人员与儿童医院科罗拉多州的睡眠医学研究人员之间的合作努力是开发非侵入性呼吸监测的第一步。在睡眠期间测量呼吸的一种新型的非侵入性装置将是必要的基础研究的第一步，以促进儿童住所舒适地诊断SDB的未来诊断，而没有住院实验室费用或可支配医疗设备的费用，每种睡眠实验室每年都等于每年数千美元。该项目还提供了一种很好的方法来培训研究生来开展此基于愿景的研究项目。放射性次数概念是构建各种教育活动的令人兴奋且具有吸引力的工具。此外，该项目的结果将通过学术出版物和我们现有的潜在工业伙伴的积极宣传来传播。