SCH: INT: Collaborative Research: DeepSense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine

SCH：INT：合作研究：DeepSense：零努力表型感知的可解释深度学习及其在睡眠医学中的应用

• 批准号: 2313481
• 负责人: M Brandon Westover
• 金额: $ 40万
• 依托单位: Beth Israel Deaconess Medical Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313481&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; DeepSense

Sleep represents one third of everyone’s life and affects the quality and the health of everyone’s life. Traditionally, long-term characteristics of sleep patterns (sleep phenotypes) are largely unknown due to the lack of convenient monitoring devices and automatic algorithms. Recently, massive health sensing data such as activity data, electroencephalogram, respiratory monitoring data and electrocardiography are being collected in clinics and at home, which brings unprecedented opportunities for understanding sleep phenotypes outside clinics. However, there are tremendous challenges to translate these noisy and unreliable multimodal sensing data into accurate phenotypes such as sleep stages and apnea events. Beyond sleep, many neurological conditions such as Alzheimer’s and Parkinson’s all expect objective tracking of long-term disease progression, which is currently impossible. This project will provide the computational capability to conduct phenotype tracking at home with the focus on sleep phenotypes. Machine learning methods and software will be developed to conduct accurate phenotyping of sleep with minimal effort on sensor instrumentation, data collection and analysis. This project aims at developing DeepSense, a deep learning toolbox to model massive data streams including in-clinic monitoring data such as polysomnography and novel radio frequency signals from a wireless sensing device. The research team will develop accurate deep learning methods to automate sleep monitoring using polysomnography data. They will invent adversarial deep learning methods for modeling radio frequency signals and leverage large historical polysomnography data to help improve models for radio frequency signals. They will develop interpretable models that leverage and expand medical knowledge on sleep phenotypes. Finally, all the proposed models will be validated through a prospective study with a goal of automating manual sleep studies and assessing the feasibility of sleep studies via radio frequency signal data. The research team plans to release the open-source software and large datasets from this project that can benefit computer science, engineering and medical community.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.

睡眠占每个人生命的三分之一，影响着每个人的生活质量和健康。传统上，由于缺乏方便的监测设备和自动算法，睡眠模式的长期特征（睡眠表型）在很大程度上是未知的。最近，大量的健康感知数据，如活动数据、脑电图、呼吸监测数据和心电图正在诊所和家庭中收集，这为了解诊所外的睡眠表型带来了前所未有的机会。然而，将这些嘈杂且不可靠的多模态传感数据转化为准确的表型（如睡眠阶段和呼吸暂停事件）存在巨大挑战。除了睡眠之外，许多神经系统疾病，如阿尔茨海默病和帕金森病，都需要对疾病的长期进展进行客观的跟踪，这在目前是不可能的。该项目将提供在家中进行表型跟踪的计算能力，重点关注睡眠表型。将开发机器学习方法和软件，以便在传感器仪器，数据收集和分析方面花费最少的精力来进行准确的睡眠表型。该项目旨在开发DeepSense，这是一个深度学习工具箱，用于模拟大量数据流，包括临床监测数据，如多导睡眠图和来自无线传感设备的新型射频信号。研究小组将开发准确的深度学习方法，利用多导睡眠图数据自动监测睡眠。他们将发明用于射频信号建模的对抗性深度学习方法，并利用大量历史多导睡眠图数据来帮助改进射频信号模型。他们将开发可解释的模型，利用和扩展睡眠表型的医学知识。最后，所有提出的模型将通过一项前瞻性研究进行验证，该研究的目标是自动化手动睡眠研究，并通过射频信号数据评估睡眠研究的可行性。研究小组计划发布该项目的开源软件和大型数据集，使计算机科学、工程和医学界受益。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。