SBIR Phase I: Non-invasive Closed Loop Neuromodulation to Treat Obstructive Sleep Apnea

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是一种非侵入性，外戴式设备，用于治疗阻塞性睡眠呼吸暂停（OSA），这种疾病影响了超过8800万美国人（占美国成年人口的26%）。受阻塞性睡眠呼吸暂停影响的人有患严重合并症的风险，如糖尿病、中风和心脏病。由于对目前的治疗方案不耐受，许多患者仍未得到治疗，坚持和遵守率低至40%。经济影响估计为300亿美元，每年由于汽车和工作场所事故以及生产力损失造成的间接总成本为1500亿美元。这项技术旨在占领180亿美元的睡眠设备市场的一部分，由于仅在美国就有大约8000万未确诊病例，该市场仍未得到充分渗透。这个小企业创新研究（SBIR）第一阶段项目旨在开发一种非侵入性的牙科神经刺激装置，能够以一种可控的、不可察觉的方式激活运动神经纤维，这些运动神经纤维供应负责扩张上呼吸道的肌肉。该设备将集成多个传感和刺激电极，以激活精确的神经分支，以便在不直接与神经分支本身连接的情况下，为相关的上气道肌肉群提供连续的神经控制。采用机器学习的算法将用于处理神经电极反馈信号和控制电场刺激波形。该项目将包括设备设计、台式测试和患者夜间睡眠研究，以建立数据集和构建新的算法。然后将开发一个软件应用程序来实时自动化例行程序，以证明一种新的非侵入性治疗阻塞性睡眠呼吸暂停的概念可行性。该奖项反映了美国国家科学基金会的法定使命，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 I project is a non-invasive, externally worn appliance for treating obstructive sleep apnea (OSA), a condition affecting over 88 million Americans (26% of the American adult population). Those impacted by OSA are at risk of serious comorbidities such as diabetes, stroke, and heart disease. Many sufferers remain untreated due to intolerance to current treatment options with adherence and compliance rates as low as 40%. The economic impact is estimated at $30 billion resulting in $150 billion aggregate indirect costs due to motor and workplace accidents as well as productivity losses each year. The technology aims to capture part of the $18 billion sleep device market which remains significantly under penetrated due to approximately 80 million undiagnosed cases in the US alone. This Small Business Innovation Research (SBIR) Phase I project aims to develop a non-invasive, dental neurostimulation device capable of activating the motor nerve fibers supplying the muscles responsible for dilating the upper airway in a controlled, non-perceptible manner. The appliance will be integrated with multiple sensing and stimulation electrodes to activate precise nerve branches in order to provide continuous innervation of relevant upper airway muscle groups, without interfacing directly with the nerve branch itself. Algorithms employing machine learning will be used to process neural electrode feedback signals and control electrical field stimulation waveforms. The project will consist of appliance design, benchtop testing, and overnight sleep studies in patients to build datasets and construct new algorithms. A software application will then be developed to automate routines in real-time in order to demonstrate concept feasibility of a new non-invasive therapy for obstructive sleep apnea.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.