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

这个小型企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是帮助患有心肺疾病的患者，包括睡眠呼吸暂停和心律失常。该项目将开发一个可穿戴传感器平台，以同时诊断和监测条件。通过舒适的无线传感器贴片收集多个生理测量值，从而形成方便的远程监测临床框架。将传感器数据与基于云的高效供应商门户和自动化算法相结合，将能够快速筛查美国2400万未确诊的睡眠呼吸暂停患者。拟议的创新还将提供对通过将多个共病或其他相关诊断途径捆绑到单个工作流程中所获得的实际临床益处和效率的深入了解，例如减少共病房颤的治疗时间。这种远程监控捆绑包概念代表了唯一一种能够以一种对患者和提供者都不显眼和用户友好的方法服务于多种高度普遍的健康挑战的一体化设备-特别是对于全球流行病更加紧迫的远程医疗应用。这个小型企业创新研究（SBIR）第二阶段项目旨在开发一种简单，准确，该公司正在开发云连接的可穿戴贴片，并收集临床比较数据，以开发自动化、低功耗算法，从而同时检测睡眠呼吸障碍、睡眠阶段和心律失常。该项目集成了材料科学、机械工程和信号处理方法，以检测躯干的关键生理信号，包括氧饱和度和几个血液动力学指标。该项目还将开展研究，为心脏和睡眠医学专业的捆绑工作流程的临床效益提供早期见解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to help patients with cardiorespiratory conditions, including sleep apnea and cardiac arrhythmia. The project will develop a wearable sensor platform to simultaneously diagnose and monitor conditions. Multiple physiological measurements are collected by a comfortable, wireless sensor patch, resulting in a convenient remote monitoring clinical framework. Interfacing sensor data with an efficient cloud-based provider portal and automated algorithms will enable rapid screening of the 24 million undiagnosed sleep apnea patients in the United States. The proposed innovation will also provide insight into the practical clinical benefits and efficiencies to be gained by bundling multiple comorbid or otherwise related diagnostic pathways into a single workflow, such as reducing time to treatment for comorbid atrial fibrillation. This remote monitoring bundle concept represents the only all-in-one device capable of servicing multiple highly pervasive health challenges in a method unobtrusive and user-friendly for both the patient and the provider - particularly for telemedicine applications made more urgent by the global pandemic.This Small Business Innovation Research (SBIR) Phase II project aims to develop a simple, accurate, cloud-connected wearable patch and collect clinical comparison data to develop automated, low-power algorithms to simultaneously detect sleep-disordered breathing, sleep stages, and cardiac arrhythmias. The project integrates materials science, mechanical engineering, and signal processing approaches to detect critical physiological signals from the torso, including oxygen saturation and several hemodynamic metrics. The project will also conduct studies that offer early insights into the clinical benefits of bundled workflows across cardiac and sleep medicine specialties.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.