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

这一创新技术转化伙伴关系(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的精确度。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，假阴性率很高。80%的中度到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状况和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。该框架是首批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，它可以以较低的成本将筛查准确率提高40%。拟议的项目还包括一个更广泛的参与者计划。为了进一步加强对所有背景的学生的包容，PFI-TT项目将利用现有的项目与学校和亚利桑那大学的项目。将为代表性不足的群体，特别是妇女、西班牙裔、非裔美国人和美洲原住民，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。我们项目的成功可以通过亚利桑那大学的Desire2Learn项目在国内和全球范围内推广到其他机构。建议的项目提供了一个新的睡眠呼吸暂停筛查框架，可以满足低成本的需求，以及高精度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能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在具有非易失性存储器和低能耗蓝牙的单一平台上整合了多个传感器。新的深度学习网络模型根据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。新模型包括双向长期-短期记忆层和完全连接的层，以探索和提取感觉数据以及静态特征，如用户的人口统计信息、现有的慢性病和总体幸福感。开发了一个后处理程序，将深度神经网络输出的睡眠呼吸暂停概率转换为使用最大概率的睡眠呼吸暂停严重程度。还开发了一款基于智能手机的应用程序，用于执行深度神经网络算法并显示睡眠呼吸暂停筛查结果。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 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.