Explainable Deep Learning Approach for Automatic Arousal and Sleep Stages Scoring, and Knowledge Discovery

用于自动唤醒和睡眠阶段评分以及知识发现的可解释深度学习方法

• 批准号: 10291585
• 负责人: Ao Li
• 金额: $ 11.51万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10291585
• 关键词: Address; Algorithms; Arousal; Artificial Intelligence; Attention; Blood Pressure; Cardiac health; Clinic; Clinical; Coronary Arteriosclerosis; Data; Databases; Dependence; Diabetes Mellitus; Diagnosis; Diagnostic; Engineering; Evaluation; Event; Frequencies; Goals; Heart failure; Home; Home environment; Human; Hypertension; Knowledge; Knowledge Discovery; Lead; Length; Machine Learning; Manuals; Mediating; Medical Care Team; Medicine; Modeling; Monitor; Multi-Ethnic Study of Atherosclerosis; Outcome; Pattern; Performance; Physicians; Physiological; Polysomnography; Prevalence; Psychological Transfer; REM Sleep; Research; Risk Factors; Scientist; Severities; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Disorders; Sleep Fragmentations; Sleep Stages; Source; Stroke; System; Techniques; Technology; Testing; Training; Trust; Woman; automated algorithm; base; convolutional neural network; deep learning; deep learning algorithm; design; heart rate variability; improved; interdisciplinary approach; learning network; men; multi-task learning; multitask; neural patterning; non rapid eye movement; portability; recurrent neural network; relating to nervous system; respiratory; sleep quality; usability

Project Summary Sleep-disordered breathing (SDB) is potential remedial risk factor for hypertension, diabetes, stroke, coronary artery disease, and heart failure. The prevalence of SDB is estimated to be between 6.5% and 9% in women and between 17% and 31% in men. During polysomnography, which is often required for diagnosis, sleep stages and the frequency of cortical arousals are important metrics. A high frequency of arousals is indicative of sleep fragmentation. Additionally, cortical arousal events are also used to identify hypopneic events in sleep scoring. Currently, type III portable sleep monitors are commonly used for diagnosing SDB severity instead of more expensive polysomnography. However, most portable home sleep test (HST) monitors do not record electroencephalographic (EEG) data which are required for arousal identification, resulting in an underestimation of SDB severity in manual scoring of SDB events. Thus, there is a critical need to improve portable HST sleep monitors with advanced automatic scoring algorithms that can identify arousals associated with SDB events. Studies have found that cortical arousal is associated with sympathetic neural surges observed on electrocardiographic (ECG) and blood pressure signals. Additionally, changes in respiratory patterns, which can be observed from the ECG signal, have been found to be associated with specific EEG patterns. Furthermore, different autonomic neural patterns dominate in non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. The RR interval and respiratory- mediated HF components of heart rate variability (HRV) increase from stages N1 to N3. Our hypothesis is that ECG signals can be used to automatically scoring sleep stages and arousals in HST. In this study, we plan to develop a deep learning-based multi-task learning algorithm for automatic arousal and sleep stage scoring. Instead of HRV based algorithms, we propose to employ an end-to-end deep learning network to acquire features from the raw ECG data. The proposed model consists of convolutional neural networks, recurrent neural networks, and an attention mechanism. It can: (1) accept varying length ECG data; (2) capture long-range dependencies in the ECG data; and (3) share knowledge among scoring tasks for arousal and sleep stages. We use HRVs to further analyze the ECG regions selected by the deep learning model. This is a critical step to understand the underpinnings of associations between sleep events and the ECG signal discovered by the proposed model. Our specific aims include: (1) developing an end-to-end multitask deep learning model for automatic arousal and sleep stages scoring by analyzing a modified lead II ECG signal which is commonly used in sleep studies; (2) advanced interpretation of deep learning model outcomes. Our current effort will evaluate the usability of deep learning approach in sleep medicine and will have a substantive and sustained impact on diagnosis outcomes for sleep disorders.

项目摘要 睡眠呼吸呼吸（SDB）是高血压，糖尿病，中风， 冠状动脉疾病和心力衰竭。 SDB的患病率估计在6.5％至9％之间 在女性中，男性在17％至31％之间。在多摄影过程中，通常需要 诊断，睡眠阶段和皮质唤醒的频率是重要的指标。高频 唤醒表明睡眠破碎。此外，皮质唤醒事件还用于识别 睡眠评分中的脱头事件。目前，III类便携式睡眠监视器通常用于 诊断SDB严重程度，而不是更昂贵的多聚疗法。但是，大多数便携式家庭睡眠 测试（HST）显示器未记录唤醒所需的脑电图（EEG）数据 识别，导致SDB事件的手动评分中SDB严重性的低估。因此， 具有高级自动评分算法的提高便携式HST睡眠监视器的迫切需要 可以确定与SDB事件相关的唤醒。研究发现皮质唤醒是相关的 在心电图（ECG）和血压信号上观察到交感神经激增。 另外，已经发现可以从ECG信号观察到呼吸模式的变化 与特定的脑电图模式相关联。此外，不同的自主神经模式在 非比型眼动（NREM）和快速眼动（REM）睡眠。 RR间隔和呼吸 心率变异性（HRV）的介导的HF成分从N1阶段增加到N3。我们的假设是 该心电图信号可用于在HST中自动评分睡眠阶段和唤醒。在这项研究中，我们 计划为自动唤醒和睡眠阶段开发基于深度学习的多任务学习算法 得分。我们建议使用端到端的深度学习网络，而不是基于HRV的算法 从RAW ECG数据中获取功能。提出的模型由卷积神经网络组成， 复发性神经网络和注意力机制。它可以：（1）接受不同长度的ECG数据； （2） 在ECG数据中捕获长期依赖性； （3）在唤醒的评分任务中分享知识 和睡眠阶段。我们使用HRV进一步分析深度学习模型选择的心电图区域。 这是了解睡眠事件与ECG之间关联的基础的关键步骤 由提出的模型发现的信号。 我们的具体目的包括：（1）开发一个端到端的多任务深度学习模型 通过分析改良的铅I II ECG信号，自动唤醒和睡眠阶段得分 用于睡眠研究； （2）深度学习模型结果的高级解释。我们目前的努力将 评估睡眠医学中深度学习方法的可用性，并将具有实质性和持续性 对睡眠障碍诊断结果的影响。