This study introduces a novel, rich dataset obtained from home sleep apnea tests using the FDA-approved WatchPAT-300 device, collected from 7,077 participants over 21,412 nights. The dataset comprises three levels of sleep data: raw multi-channel time-series from sensors, annotated sleep events, and computed summary statistics, which include 447 features related to sleep architecture, sleep apnea, and heart rate variability (HRV). We present reference values for Apnea/Hypopnea Index (AHI), sleep efficiency, Wake After Sleep Onset (WASO), and HRV sample entropy, stratified by age and sex. Moreover, we demonstrate that the dataset improves the predictive capability for various health related traits, including body composition, bone density, blood sugar levels and cardiovascular health. These results illustrate the dataset's potential to advance sleep research, personalized healthcare, and machine learning applications in biomedicine.

本研究介绍了从家庭睡眠呼吸暂停监测中获得的富有价值的数据集，该数据集使用FDA认证的WatchPAT-300设备，收集了7,077名参与者在21,412个夜晚的数据，包括原始的传感器多通道时间序列，注释的睡眠事件以及与睡眠结构、睡眠呼吸暂停和心率变异性相关的447个特征的计算摘要统计信息。我们还介绍了按照年龄和性别分层的AHI、睡眠效率、入睡后唤醒时间以及心率变异性样本熵的参考值。此外，我们还证明了该数据集在包括身体成分、骨密度、血糖水平和心血管健康等各种与健康相关的特征的预测能力方面的改进。这些结果展示了该数据集在推动睡眠研究、个性化医疗以及生物医学中的机器学习应用方面的潜力。

一个包含21,412个记录的多模态睡眠与呼吸研究数据集