Dissertation

Sleep Health and Wearable Technology: Algorithmic Development Towards Field-Based Sleep Monitoring

Wang, Will Ke

2024

Overview

Wearable devices have rapidly become essential tools for tracking sleep in natural, nonclinical settings. Despite their widespread adoption, consumer-grade wearables, such as smartwatches and fitness trackers, exhibit significant limitations in their ability to accurately track wake epochs after sleep onset and classify sleep stages, particularly in individuals with sleep disorders. Recent independent validation studies reported frequent misclassifications, especially distinguishing Rapid Eye Movement (REM) sleep and non-REM (NREM) N3 from other sleep stages. This inaccuracy is exacerbated in populations with sleep disorders such as obstructive sleep apnea (OSA), where sleep is fragmented and physiological signals and sleep patterns deviate from those seen in healthy individuals. Furthermore, it is practically impossible for independent academic researchers to develop and evaluate sleep staging algorithms from consumer devices due to their proprietary nature and a lack of publicly available datasets for wearable sleep staging. The need for precise, reliable, and scalable sleep tracking methods in wearable devices is crucial as wearables become more integrated into both personal health management and clinical applications.To address these challenges, I collected and published the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology (DREAMT), a unique collection of multimodal physiological data recorded from 100 participants diagnosed with varying sleep disorders at the Duke Sleep Disorders Center. The DREAMT dataset includes synchronized recordings of both wearable device data, obtained using Empatica E4 smartwatches, and sleep stage annotations and sleep apnea events annotated by certified sleep technicians based on clinical polysomnography (PSG), the gold standard for clinical sleep studies and sleep staging. This dataset is the first and only high-resolution wearable smartwatch dataset with reliable sleep stages made public. It is an indispensable resource for advancing the development and validation of sleep staging algorithms capable of accurately detecting sleep stages using wearable smartwatches by serving as a benchmark for the research community to develop and compare new algorithmic development. It represents an important step towards establishing an open science framework for wearable-based sleep research.Leveraging this dataset, I proposed a modeling approach to predict sleep vs. wake, combining feature engineering, Light Gradient Boosting Machines (LightGBM) with Gaussian Process-based mixed effects modeling (GPBoost) for epoch-by-epoch sleep vs. wake prediction, and a Long Short-Term Memory (LSTM) network for post-processing. The LSTM module is an innovative approach to improve sleep vs. wake detection by capturing the temporal dependencies within these physiological signals. This feature engineering process significantly enhanced the model’s ability to detect transitions between wakefulness and sleep, especially in cases of individuals with sleep disorders by recognizing that individuals of varying degrees of sleep disorder severity are very likely to exhibit different sleep patterns and behaviors. This ensemble model established a baseline and also provided a foundation for exploring more sophisticated deep learning architectures tailored to wearable sleep data.Building on this foundation and to utilize the existing large external PSG datasets, I designed WatchSleepNet to predict wake vs. NREM vs. REM, a deep learning model specifically developed to tackle the inherent challenges of wearable-based sleep staging. WatchSleepNet integrates Convolutional Neural Networks (CNNs), Temporal Convolutional Networks (TCNs), and bidirectional LSTM networks with multi-head attention mechanisms to process Inter-beat Interval (IBI) signals for sequence-to-sequence classification. The model was trained to recognize both spatial and temporal dependencies in the physiological data, enabling it to accurately classify sleep stages in terms of wake, NREM, and REM. One of the unique strengths of WatchSleepNet is the approach of pretraining using IBI values calculated from both ECG and PPG signals available in large external PSG datasets, including the Sleep Heart Health Study (SHHS) and the Multi-Ethnic Study of Atherosclerosis (MESA). This pretraining step allowed the model to learn foundational patterns in sleep physiology across diverse populations and levels of sleep disorder severity. Following pretraining, the model was fine-tuned using the DREAMT dataset, ensuring that it could adapt to the unique characteristics of wrist-based PPG data collected in real-world settings. WatchSleepNet demonstrated superior performance compared to state-of-the-art models like SleepConvNet and InsightSleepNet in a similar training pipeline, achieving significant improvements in REM sleep classification, an area where consumer-grade wearables typically perform poorly. The model achieved a REM F1-score of 0.648 and a Cohen’s Kappa of 0.706, significantly higher than the results from the benchmark algorithms, highlighting its potential to bridge the gap between consumer and gold-standard in-clinic sleep tracking.Beyond model development, this dissertation contributes to the broader field of digital health, particularly in promoting open science and the standardization of wearable sensor data for sleep research. By publishing the DREAMT dataset and developing reproducible methodologies for digital sleep biomarkers, this work sets the stage for more transparent, collaborative research in wearable-based sleep tracking. Additionally, I explored the clinical utility of wearable sleep monitoring in detecting circadian rhythm disruptions and their impacts on mental health in adolescents. Circadian misalignments, common among shift workers and individuals with sleep disorders, are linked to an increased risk of mood disorders, anxiety, and metabolic dysfunction. By advancing the accuracy and reliability of wearable devices in tracking these disruptions, this research opens new avenues for early detection, intervention, and management of these conditions.

• Publisher: ProQuest Dissertations & Theses
• Subject: Artificial intelligence / Bioinformatics / Biomedical engineering
• ISBN: 9798302161611