Wearanize+:用于评估睡眠研究中的可穿戴技术的多式联络数据集
Niloy Sikder1,2, Lieuwe Verkaar1, Anastasiya Paltarzhytskaya1
1Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
Sleep advances : a journal of the Sleep Research Society
|February 12, 2026
概括
这项研究介绍了Wearanize+数据集,将多睡眠学与多个可穿戴设备相结合,以可靠地监测睡眠. 它旨在开发机器学习模型,从消费者级技术中准确地分阶段睡眠.
科学领域:
- 睡眠科学和生物医学工程.
- 消费者睡眠技术研究.
- 医疗保健中的数据科学.
背景情况:
- 多睡眠学 (PSG) 是睡眠研究的黄金标准,但资源密集型.
- 可穿戴设备提供方便的睡眠监测,但往往缺乏数据质量和可靠分析的频道数量.
- 结合来自多个可穿戴设备的数据,为克服单个设备的局限性提供了一个潜在的解决方案.
研究的目的:
- 介绍和描述Wearanize+数据集,这是一个同时进行多睡眠和多可穿戴设备记录的新集合.
- 促进机器学习模型的开发,以便从可穿戴数据中获得PSG级睡眠阶段.
- 探索用于睡眠阶段评分的替代数据模式,特别是使用噪音高的脑电图 (EEG) 信号.
主要方法:
- 收集了130名健康参与者使用PSG和三种可穿戴设备 (Zmax头带,Empatica E4手腕带,ActivPAL腿部贴片) 的夜间睡眠记录.
- 在家庭环境中获取数据,确保生态有效性.
- 记录数据集的设置,数据收集程序和可重复性预处理步骤.
主要成果:
- Wearanize+数据集为验证多可穿戴睡眠监测方法提供了一个全面的资源.
- 该数据集可以研究机器学习算法,用于使用可穿戴传感器数据进行睡眠阶段分类.
- 它支持对睡眠分析的替代数据源的调查,当传统的EEG受到损害时.
结论:
- Wearanize+数据集是利用可穿戴技术推进睡眠研究的宝贵资源.
- 这一数据集可以加速开发可靠的多设备睡眠监测系统.
- 它为更容易访问和可扩展的睡眠研究和临床应用铺平了道路.
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