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

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是一种治疗阻塞性睡眠呼吸暂停(OSA)的非侵入性外部穿戴设备，这种疾病影响着超过8800万美国人(占美国成年人口的26%)。那些受到阻塞性睡眠呼吸暂停综合症影响的人有患严重并发症的风险，如糖尿病、中风和心脏病。由于对目前的治疗方案不耐受，许多患者仍然没有得到治疗，依从性和遵从率低至40%。经济影响估计为300亿美元，每年因电机和工作场所事故以及生产力损失而造成的间接成本总计为1500亿美元。这项技术旨在占领价值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8000万例未确诊病例，该市场的渗透率仍然很低。这个小型企业创新研究(SBIR)第一阶段项目旨在开发一种非侵入性的牙科神经刺激设备，能够激活运动神经纤维，以受控、不可感知的方式供应负责扩张上呼吸道的肌肉。该装置将与多个感应和刺激电极集成在一起，以激活精确的神经支，以便为相关的上呼吸道肌群提供持续的神经支配，而不直接与神经支本身对接。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。该项目将包括电器设计、台式测试和患者过夜睡眠研究，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程，以展示阻塞性睡眠呼吸暂停新的非侵入性治疗的概念可行性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.