SBIR Phase I: Single wearable patch for cost-effective, reliable, and accurate home sleep apnea testing

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将调查新型可穿戴传感器技术的可行性，以提供一个完整的诊断评估阻塞性睡眠呼吸暂停（OSA）与一个单一的无线贴片佩戴在胸部。据估计，美国有2500万至3000万成年人患有OSA，但80%的OSA阳性患者未被诊断出来，而最高容量的诊所每年只能进行约5,000次睡眠测试。诊断率低的部分原因是睡眠诊所管理昂贵而复杂的家庭睡眠测试设备的挑战。到目前为止，所有的家庭睡眠测试都需要患者佩戴笨重的传感器，这些传感器通过电线、管道和探针网络连接到他们身体的多个（3-5个）区域。该项目将探索新颖、不显眼、具有成本效益的传感器，以可靠地检测睡眠期间的呼吸暂停和呼吸不足。该项目的成果将加强对睡眠呼吸暂停期间生理信号如何改变以及如何使用简单的可穿戴贴片可靠地测量这些信号的基本理解。拟议的SBR第一阶段项目将推进用于诊断阻塞性睡眠呼吸暂停的无线设备的传感器平台的开发。该项目将探索新的传感模式的可行性，以便通过单个无线贴片提供足够的诊断信息。创新源于：（1）使用新型心肺传感模式和机器学习来确定呼吸事件和呼吸紊乱引起的觉醒，以及（2）将多种传感模式集成到胸戴式贴片中，以根据已建立的临床指南提供对睡眠呼吸暂停严重程度的完整评估。该项目将：进行与临床标准有线技术相关的传感器模态的功能测试;使用先进的数字信号处理技术识别呼吸紊乱的数字签名;并评估完整的单一贴片传感器相对于临床金标准睡眠测试的诊断能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will investigate the feasibility of novel wearable sensor techniques to provide a complete diagnostic assessment for obstructive sleep apnea (OSA) with a single wireless patch worn on the chest. An estimated 25-30 M adults in the United States have OSA, but 80% of OSA-positive patients are undiagnosed while the highest volume clinics can only conduct around 5,000 sleep tests annually. Low diagnosis rates result partly from the challenge of sleep clinics managing an inventory of expensive and complex home sleep testing devices. To date, all home sleep tests require patients to wear bulky sensors that attach to multiple (3-5) regions of their body via a network of wires, tubes, and probes. This project will explore novel, unobtrusive, cost-effective sensors to reliably detect apnea and hypopnea during sleep. The project outcomes will strengthen fundamental understanding of how physiological signals are altered during sleep apnea and how to reliably measure these signals using simple wearable patches.The proposed SBR Phase I project will advance the development of a sensor platform for a wireless device to diagnose obstructive sleep apnea. The project will explore the feasibility of new sensing modalities to provide sufficient diagnostic information with a single wireless patch. The innovation stems from: (1) use of novel cardiorespiratory sensing modalities and machine learning to determine respiratory events and arousals from disordered breathing, and (2) integration of multiple sensing modalities into a chest-worn patch to provide a complete assessment of sleep apnea severity according to established clinical guidelines. This project will: conduct functional tests of sensor modalities relative to the clinical standard wired technologies; identify digital signatures of disordered breathing using advanced digital signal processing techniques; and assess the diagnostic capability of the complete single patch sensor relative to the clinical gold standard sleep tests.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.