PFI-AIR-TT: Wearable sleepwear for quantitative prognostication and noninvasive therapy of obstructive sleep apnea

PFI-AIR-TT：可穿戴睡衣，用于阻塞性睡眠呼吸暂停的定量预测和无创治疗

• 批准号: 1543226
• 负责人: Satish Bukkapatnam
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1543226&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Wearable; sleepwear

This PFI: AIR Technology Translation project focuses on translating an advanced prediction and prognostics approach to fill the need for affordable and effective at-home solutions to treat sleep disorders. Sleep disorders such as obstructive sleep apnea (OSA) are prevalent in an estimated 6.6% of the national population. The market for sleep treatment is noted to exceed $23 billion per year, and is set to grow at over 15% annually. However the current treatment procedures suffer from severe drawbacks and are highly criticized in consumer forums. Recent advancements in sensors and wireless communication offer an unprecedented opportunity for cost-effective home care options to treat sleep apnea. This project will result in a prototype of SleepEaze technology that captures this opportunity. This technology is based on using a wireless biometric sleepwear to monitor the apnea patient's biorhythms during sleep, and it employs a patent-pending algorithm to predict apnea events several minutes ahead of their onset. Then the muscles responsible for the obstruction can be stimulated when the event is predicted to be imminent ("nip in the bud"), allowing the patient a more restful sleep. The use of the innovative prediction algorithm as well as low power electronic elements offer a breakthrough in the noninvasive stimulations that can lead to a new class of affordable wearable devices for sleep apnea treatment with high commercialization potential.This project plans to pursue three major objectives: (a) Technology Improvement, which includes: the refinement of the wearable sensor layout to enhance comfort, tuning of parameters of the prediction model based on nonparametric Dirichlet Process Mixture of Gaussian Process representations to reduce computational overhead associated with multi-step look ahead procedures, and optimization of the stimulation modality, location, and time-frequency stimulation profiles using a novel low power electronics to effectively avert sleep apnea events; (b) Extended testing using an early prototype, extending an ongoing human subject study to benchmark signal quality, prediction and stimulation performance, and sleep quality improvement from the technology in preparation for approvals (510K) from the federal regulatory agencies; and (c) Prototyping of a updated wearable multi-sensor unit for real-time treatment of sleep apnea episodes, as well as its testing and benchmarking. If successful, the proposed project will result in a functional, refined prototype of the SleepEaze device (hardware and associated software interface) that offers a radically new approach for OSA treatment. In addition, personnel involved in this project, including a post-doctoral associate and two graduate students, will receive entrepreneurship experiences offered through Texas A&M University and the NSF Southwest I-Corps node.

PFI：空气技术翻译项目着重于翻译高级预测和预后方法，以满足负担得起有效的家庭解决方案来治疗睡眠障碍的需求。估计占全国人口的6.6％，诸如阻塞性睡眠呼吸暂停（OSA）之类的睡眠障碍普遍存在。睡眠治疗市场每年超过230亿美元，每年的增长率超过15％。 但是，当前的治疗程序遭受了严重的缺点，并且在消费者论坛中受到了高度批评。传感器和无线通信的最新进展为具有成本效益的家庭护理选择提供了前所未有的机会来治疗睡眠呼吸暂停。该项目将导致捕获这一机会的SleepeAze技术的原型。这项技术基于使用无线生物识别睡衣来监测睡眠期间呼吸暂停患者的生物节律，并且它采用了一种申请专利的算法来预测其发作前几分钟的呼吸暂停事件。然后，当事件被预测为即将到来时（“芽中的nip”），可以刺激导致障碍物的肌肉，从而使患者更静止地睡眠。 The use of the innovative prediction algorithm as well as low power electronic elements offer a breakthrough in the noninvasive stimulations that can lead to a new class of affordable wearable devices for sleep apnea treatment with high commercialization potential.This project plans to pursue three major objectives: (a) Technology Improvement, which includes: the refinement of the wearable sensor layout to enhance comfort, tuning of parameters of the prediction model based on高斯过程表示的非参数dirichlet过程混合物，以减少与多步查看未来过程相关的计算开销，并优化刺激方式，位置和时频刺激曲线，使用新型的低功率电子设备有效地避免睡眠呼吸暂停事件； （b）使用早期原型进行扩展测试，将正在进行的人类学科研究扩展到基准信号质量，预测和刺激性能，并从技术中改善了来自联邦监管机构的批准（510K）的睡眠质量； （c）原型制作更新的可穿戴多传感器单元，用于对睡眠呼吸暂停发作的实时处理及其测试和测试测试。如果成功，则提议的项目将导致SleepeAze设备（硬件和相关软件接口）的功能性，精致的原型，该原型为OSA处理提供了根本新的方法。此外，参与该项目的人员，包括博士后同学和两名研究生，将获得通过德克萨斯A＆M大学和NSF西南I-Corps节点提供的企业家经验。