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

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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.