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 睡眠呼吸障碍(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 女性为17%至31%，男性为17%至31%。在多导睡眠监测期间，这通常是 诊断、睡眠阶段和大脑皮层觉醒的频率是重要的指标。频率很高的 性唤醒是睡眠支离破碎的表现。此外，皮质觉醒事件也被用来识别 睡眠评分中的低睡眠事件。目前，III型便携式睡眠监护仪通常用于 诊断SDB严重程度，而不是更昂贵的多导睡眠图。然而，大多数便携式家庭睡眠 测试(HST)监测器不记录唤醒所需的脑电(EEG)数据 识别，导致在SDB事件的手动评分中低估了SDB的严重性。因此， 迫切需要用先进的自动评分算法来改进便携式HST睡眠监测器 可以识别与SDB事件相关的唤醒。研究发现，大脑皮层的唤醒与 在心电图和血压信号上观察到交感神经电涌。 此外，还发现了呼吸模式的变化，这可以从心电信号中观察到 与特定的脑电模式相关联。此外，不同的自主神经模式在 非快速眼动(NREM)和快速眼动(REM)睡眠。RR间期和呼吸- 心率变异性(HRV)的介导性HF成分从N1期到N3期增加。我们的假设是 心电信号可以用来对HST中的睡眠阶段和觉醒进行自动评分。在这项研究中，我们 计划开发一种基于深度学习的自动唤醒和睡眠阶段多任务学习算法 得分。代替基于HRV的算法，我们建议使用端到端深度学习网络来 从原始心电数据中提取特征。所提出的模型由卷积神经网络组成， 递归神经网络和注意力机制。它可以：(1)接受不同长度的心电数据；(2) 捕获心电数据中的远程依赖关系；以及(3)在唤醒评分任务之间共享知识 和睡眠阶段。我们使用心率变异性来进一步分析深度学习模型所选择的心电区域。 这是理解睡眠事件和心电图之间联系的基础的关键一步。 由所提出的模型发现的信号。 我们的具体目标包括：(1)开发一个端到端的多任务深度学习模型 通过分析修改的II导联心电信号自动进行觉醒和睡眠阶段评分 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 评估深度学习方法在睡眠医学中的可用性，并将有实质性和持续性 对睡眠障碍诊断结果的影响。