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.

睡眠代表着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（睡眠表型）的长期特征在很大程度上是未知的。活动数据、脑电图、呼吸监测数据和心电图等传感数据正在诊所和家里收集，这为在诊所外了解睡眠表型带来了前所未有的机会。然而，转化这些嘈杂且不可靠的数据面临着巨大的挑战。将多模态传感数据转化为准确的表型，例如睡眠阶段和呼吸暂停事件。除了睡眠之外，阿尔茨海默氏症和帕金森氏症等许多神经系统疾病都期望对长期疾病进展进行客观跟踪，但目前该项目将提供计算能力。将开发机器学习方法和软件，以最少的传感器仪器、数据收集和分析工作来进行准确的睡眠表型跟踪，该项目旨在开发 DeepSense，一种用于建模的深度学习工具箱。海量数据研究团队将开发精确的深度学习方法，以使用多导睡眠图数据自动监测睡眠。他们将发明用于建模射频信号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，以利用和扩展有关睡眠表型的医学知识。最后，所有提出的模型都将通过前瞻性研究进行验证，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。研究团队计划发布该项目的开源软件和大型数据集，这可以使计算机科学、工程和医学界受益。该奖项反映了 NSF 的法定奖项。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。