DREAMT: Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology

Sleep is an intrinsic part of human life, and recent advancements in wearable
technology and machine learning have promised continuous and non-invasive
methods of tracking sleep health and patterns, providing an important facet to
a more holistic understanding of well-being. However, it is still challenging
to achieve consistent and reliable real-time estimates of sleep stages using
only smartwatches. This is especially true for individuals with irregular
sleep patterns or sleep disorders. A major contributing factor is the distinct
lack of publicly accessible, large-scale datasets that allow researchers and
engineers to validate their wearable sleep staging algorithms against a
population with diverse sleep patterns. Here, we present DREAMT, _**D**_
ataset for _**R**_ eal-time sleep stage _**E**_ stim _ **A**_ tion using
_**M**_ ultisensor wearable _**T**_ echnology, a new dataset collected from
100 participants, which includes high-resolution signals from a smartwatch,
expert sleep technician-annotated sleep stage labels, and clinical metadata
related to sleep health and disorders.

睡眠是人类生活的固有组成部分。近年来，可穿戴技术与机器学习的长足进步，催生了持续无创的睡眠健康与睡眠模式监测手段，为全面认知健康福祉提供了关键维度。然而，仅依托智能手表实现睡眠分期的一致且可靠的实时估算，仍存在较大挑战。对于睡眠模式不规律或存在睡眠障碍的人群而言，这一挑战尤为显著。造成该困境的核心诱因之一，是当前公开可获取的大规模睡眠相关数据集严重匮乏——此类数据集能够支持研究人员与工程师，针对具有多样化睡眠模式的人群验证其可穿戴睡眠分期算法。
在此，我们发布DREAMT数据集，其全称为基于多传感器可穿戴技术的实时睡眠分期估算数据集（Dataset for Real-time sleep stage Estimation using Multisensor wearable Technology）。该数据集共采集自100名参与者，包含智能手表采集的高分辨率生理信号、专业睡眠技师标注的睡眠分期标签，以及与睡眠健康及睡眠障碍相关的临床元数据。

搜集汇总

背景与挑战

背景概述

DREAMT数据集是一个专注于实时睡眠分期估计的多传感器可穿戴技术数据集，包含100名参与者的数据，采集了智能手表的高分辨率信号（如BVP、加速度计、EDA和温度）以及专家标注的睡眠分期标签。该数据集旨在解决可穿戴设备睡眠分期算法缺乏公开大规模数据的问题，特别针对睡眠模式不规则或患有睡眠障碍的人群，提供了时间对齐的原始信号和临床元数据，以支持算法验证和研究。