PFI-TT: A New Deep Neural Network and Internet of Things-based Screening Framework for Sleep Apnea

该创新-技术转化合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是提高睡眠呼吸暂停筛查精度。超过2200万美国人受到睡眠呼吸暂停的影响。目前的家庭睡眠测试系统往往低估了睡眠呼吸暂停的严重程度，具有较高的假阴性率。80%的中度至重度阻塞性睡眠呼吸暂停患者从未被诊断出来。由于睡眠呼吸暂停是一种严重的医学疾病，也是一个重大的公共卫生问题，我们迫切需要一个新的睡眠呼吸暂停筛查框架。该框架是第一个使用可穿戴和物联网传感器的基于深度学习的睡眠呼吸暂停筛查框架之一。它具有商业潜力：与现有的睡眠呼吸暂停筛查设备相比，它可以潜在地将筛查准确性提高40%，且成本较低。 拟议的项目还包括一个更广泛的参与者计划。为了进一步提高来自各种背景的学生的包容性，PFI-TT项目将利用亚利桑那大学的学校和课程的现有课程。将为代表性不足的群体，特别是妇女、西班牙裔、非洲裔美国人和美洲土著人提供更多的讲习班和电话会议，以指导和指导对创业感兴趣的学生。我们计划的成功可以通过亚利桑那大学的Desire 2Learn计划在国内和全球其他机构进行调整。建议的项目提供了一个新的睡眠呼吸暂停筛查框架，可以解决低成本的需求以及高精度的睡眠呼吸暂停筛查的需求。新的睡眠呼吸暂停筛查框架的智力优势包括一个新的集成睡眠监测可穿戴传感器，两个深度神经网络算法，以及一个基于智能手机的应用程序，用于收集睡眠数据并提供筛查结果。睡眠监测可穿戴传感器将多个传感器集成在一个平台上，并具有非易失性存储器和低功耗蓝牙。新的深度学习网络模型基于呼吸暂停低通气指数（AHI）使用七个级别对睡眠呼吸暂停的严重程度进行分类。新模型包括双向长短期记忆层和完全连接层，以探索和提取感官数据以及静态特征，如用户的人口统计信息、现有慢性疾病和总体健康状况。开发了一种后数据处理程序，以使用最大概率将深度神经网络输出的睡眠呼吸暂停概率转换为睡眠呼吸暂停严重程度。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 Partnerships for Innovation - Technology Translation (PFI-TT) project is to improve sleep apnea screening precision. More than 22 million Americans are affected by sleep apnea. The current home sleep test systems tend to underestimate sleep apnea severity with a high false negative rate. Eighty percent of patients with moderate to severe obstructive sleep apnea are never diagnosed. Because sleep apnea is a serious medical condition and a major public health issue, we urgently need a new sleep apnea screening framework. The proposed framework is one of the first deep learning based sleep apnea screening framework using wearable and IoT sensors. It has commercial potential: it can potentially improve screening accuracy by 40 percent with low cost compared with existing sleep apnea screening devices. The proposed project also includes a broader participant plan. In order to further enhance inclusion of students from all backgrounds, the PFI-TT project will leverage existing programs with schools and programs at the University of Arizona. Additional workshops and teleconferences will be provided for underrepresented groups, particularly women, Hispanic, African Americans and Native Americans, to mentor and coach students with interests in entrepreneurship. The success of our program can be adapted at other institutions nationally and globally through the Desire2Learn program at the University of Arizona. The proposed project provides a new sleep apnea screening framework that can addresses the need of low cost together with the need of high precision sleep apnea screening. The intellectual merit of the new sleep apnea screening framework includes a new integrated sleep monitoring wearable sensor, two deep neural network algorithms, and a smart phone based app to collect sleep data and provide screening results. The sleep monitoring wearable sensor incorporates multiple sensors on a single platform with non-volatile memory and low energy Bluetooth. The new deep learning network models classifies the severity of sleep apnea using seven levels based on the Apnea-hypopnea index (AHI). The new models include bidirectional long-short term memory layers and fully connected layers to explore and extract sensory data together with static features such as users' demographic information, existing chronic diseases, and general wellbeing. A post data processing procedure is developed to translate the probability of sleep apnea at the output of deep neural networks to sleep apnea severity using the maximum probability. A smart phone based app is also developed to execute deep neural network algorithms and display sleep apnea screening results.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.