Oura Health, Elektroniikkatie 10, 90590 Oulu, Finland.
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands.
Sensors (Basel). 2021 Jun 23;21(13):4302. doi: 10.3390/s21134302.
Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.
消费级睡眠追踪器是大规模研究和健康管理的有前途的工具。然而，这些设备的潜力和局限性还没有得到很好的量化。针对这个问题，我们旨在对加速度计、自主神经系统（ANS）介导的外周信号和睡眠阶段检测的昼夜节律特征对大型数据集的影响进行全面分析。从一个可穿戴戒指中获取了 106 个人的 440 个夜晚，总共 3444 小时的综合多导睡眠图（PSG）和生理数据。提取了特征来研究不同数据流对 2 阶段（睡眠和清醒）和 4 阶段分类准确性（浅非快速眼动睡眠、深非快速眼动睡眠、快速眼动睡眠和清醒）的相对影响。使用 5 折交叉验证和睡眠阶段分类评估的标准化框架评估了机器学习模型的准确性。基于简单加速度计的模型的 2 阶段检测（睡眠、清醒）准确性为 94%，包含 ANS 衍生和昼夜节律特征的完整模型的准确性为 96%。基于加速度计的模型的 4 阶段检测准确性为 57%，而包含 ANS 衍生和昼夜节律特征的模型的准确性为 79%。结合手指戒指的紧凑外形、多维生物识别传感器流和机器学习，可以实现高精度的清醒-睡眠检测和睡眠分期。
