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，如果不及时发现，可能导致或增加心血管疾病、中风、代谢疾病、白天嗜睡、工作失误、交通事故和死亡的风险。据估计，全世界有10亿人，即七分之一的成年人患有阻塞性睡眠呼吸暂停症。由于睡眠主要发生在卧室里，在家里监测睡眠质量，而不是在睡眠实验室，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。因此，一种在家无创监测睡眠质量的方法将具有重大的社会和健康效益。该项目汇集了来自信息学和健康学科的领先研究人员，创建了用于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化为与OSA相关的临床有意义的生命体征和行为模式。该项目涉及SCH招标的三个主要主题：自动化健康，变革数据科学和有效可用性。该研究为计算机、数据和生物医学科学与工程做出了重要贡献，并将创建第一个非接触式物联网（loT）系统，用于实时和无参与性睡眠呼吸暂停监测和表征。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颖流数据ai /OS：基于移动和统计和递归二值分割的鲁棒信号质量控制和分割过程；一种新的因子自回归递归神经网络框架表征关键睡眠事件基于面板数据模型和回归系数结构变化的生命体征及其变化监测新方法并提出了一种灵活的分布鲁棒性特征评估方法，实现了分布外（000）泛化。拟议的跨学科研究采取协调的方法，平衡理论与循证分析和系统的进步。该项目将通过研究原型对新概念进行实证验证，范围从特定组件到整个系统，并产生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.