Jaworski Dominic, Park Edward J
Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, V3T 0A3, Canada.
WearTech Labs, Simon Fraser University, Surrey, BC, V3V 0C6, Canada.
Nat Sci Sleep. 2024 Jul 26;16:1075-1090. doi: 10.2147/NSS.S464944. eCollection 2024.
Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics.
Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable.
Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages.
Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.
可穿戴式或非接触、非侵入式设备为日常睡眠质量评估提供了一种替代传统多导睡眠图（PSG）的实用方法。众所周知，随着身体适应休息或活动状态，生理信号具有非线性和非平稳性。通过整合更复杂的非线性方法，可以提高使用此类设备进行睡眠阶段识别的准确性。这一进展使个人无需前往睡眠诊所就能更有效地监测和调整自己的睡眠模式。
六名参与者每人佩戴PSG作为参考，同时佩戴苹果手表、活动记录仪和心冲击图（BCG）床传感器，进行了三个周期、每个周期至少三小时的睡眠。生理信号采用非线性方法进行处理，并使用长短期记忆（LSTM）模型进行训练以对睡眠阶段进行分类。使用非线性方法，如采用先进技术的返回映射来分析生理信号的形状和不对称性，将这些信号与自主神经系统（ANS）联系起来。由ANS调节的安静或活跃状态下心脏信号的动态变化与睡眠阶段和质量相关，这些都是可测量的。
将BCG和苹果手表信号结合起来与PSG参考系统进行比较，以对快速眼动（REM）和非快速眼动睡眠阶段进行分类，获得了约73%的一致性。
在本研究中，利用非线性方法评估心脏动力学显示，使用非侵入式设备可提高睡眠质量检测能力。非侵入式设备系统可以通过定期测量睡眠模式和质量，随着时间的推移提供全面的健康状况，为参与者提供一种相对容易获得的方法。此外，非侵入式系统可以集成到用户或诊所的卧室环境中，以测量和评估睡眠质量，而不会对睡眠产生负面影响。放置在卧室周围的设备可以在用户极少干预的情况下长时间测量用户的生命体征，呈现其自然睡眠趋势，以便进行更准确的健康和睡眠障碍诊断。
