SCH: Contactless and Engagement-free Sleep Apnea Monitoring and Characterization

阻塞性睡眠呼吸暂停(OSA)是一个主要的健康问题，如果没有发现，可能会导致或增加心血管疾病、中风、代谢性疾病、日间嗜睡、工作场所失误、交通事故和死亡的风险。全世界估计有10亿人患有阻塞性睡眠呼吸暂停综合症，每七个成年人中就有一个人患有阻塞性睡眠呼吸暂停综合症。由于睡眠主要发生在卧室，因此在家里而不是在睡眠实验室监测睡眠质量将大大促进OSA和其他睡眠障碍的自我管理，并可能促进临床管理。此后，一种在家中非侵入性地监测睡眠质量的方法将会对社会和健康产生显著的好处。该项目汇集了信息学和健康学科的领先研究人员，创建了一种用于OSA监测和表征的非接触式传感器系统，该系统将先进的人工智能(Al)和数据科学(OS)集成到智能传感器和家庭护理中。关键的研究挑战是将信息丰富的传感器信号转换为与阻塞性睡眠呼吸暂停综合征相关的具有临床意义的生命体征和行为模式。该项目涉及SCH征集的三个主要主题：自动化健康、变革性数据科学和有效可用性。这项拟议的研究对计算机、数据和生物医学科学与工程做出了基础性贡献，并将创建第一个用于实时和无接触睡眠呼吸暂停监测和表征的非接触物联网(Lot)系统。主要的技术创新是一套用于睡眠事件和生命体征监测的新型流数据A1/OS：基于移动和统计和递归二进制分割的稳健信号质量控制和分割过程；用于表征关键睡眠事件的新型因子自回归递归神经网络框架；基于创新的面板数据模型和回归系数的结构变化来监测生命体征及其变化的新方法；以及灵活和分布式的稳健特征评估方法，以支持非分布(000)泛化。拟议的跨学科研究采用了一种协调的方法，平衡了理论与循证分析和系统进步。该项目将通过研究原型对新概念进行经验验证，范围从特定组件到整个系统，并导致新的基本见解和有效的可用性。

Obstructive sleep apnea (OSA) is a major health problem and can lead to or increase the risk of cardiovascular disease, stroke, metabolic disease, daytime sleepiness, workplace errors, traffic accidents and death, if it is left undetected. Worldwide it is estimated one billion people, one in seven adults, have OSA. As sleep occurs primarily in the bedroom, monitoring sleep quality at home, instead of in sleep labs, would significantly advance the self-management, and potentially the clinical management, of OSA and other sleep disorders. Thereafter, an approach that noninvasively monitors sleep quality at home would have significant societal and health benefits. This project brings together leading researchers from informatics and health disciplines to create a contactless sensor system for OSA monitoring and characterization, which integrates advanced Artificial Intelligence (Al) and Data Science (OS) into smart sensors and home care. The key research challenges are to convert the information-rich sensor signals to clinically meaningful vital signs and behavioral patterns that are linked with OSA. This project addresses three primary themes of the SCH solicitation: Automating Health, Transformative Data Science, and Effective Usability. The proposed research makes fundamental contributions to computer, data and biomedical science and engineering and will create the first contactless Internet of Things (loT) system for real-time and engagement-free sleep apnea monitoring and characterization. The main technological innovation is a set of novel stream data Al/OS for sleep events and vitals monitoring: a robust signal quality control and segmentation process based on a moving-sum statistic and recursive binary segmentation; a novel factor auto-regressive recurrent neural network framework to characterize key sleep events; a new approach of monitoring vital signs and their variations based on an innovative panel data model and the structural changes in regression coefficients; and a flexible and distributional robust feature assessment method to enable out-of-distribution (000) generalization. The proposed interdisciplinary research takes a coordinated approach that balances theory with evidence-based analysis and systematic advances. The project will conduct empirical validation of new concepts through research prototypes, ranging from specific components to entire systems, and lead to new fundamental insights and effective usability.