Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation.
Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation.
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开源纵向睡眠分析从加速度计数据（DPSleep）：算法开发和验证。
DOI：
10.2196/29849
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发表时间：
2021-10-06
影响因子：
5
通讯作者：
Buckner RL
中科院分区：
文献类型：
--
作者：
Rahimi-Eichi H;Coombs Iii G;Vidal Bustamante CM;Onnela JP;Baker JT;Buckner RL
可穿戴设备现在广泛用于从个人收集连续客观的行为数据并测量睡眠。这项研究旨在引入一个管道，从原始加速度计数据中推断睡眠开始、持续时间和质量，然后量化推导出的睡眠指标与其他感兴趣变量之间的关系。这里发布的用于睡眠深度表型分析的管道，作为DPSAND软件包，使用逐步算法来检测缺失数据;个体内，基于分钟的活动频谱功率谱;以及迭代，向前和向后滑动窗口来估计主要睡眠发作的开始和偏移。软件模块允许对导出的睡眠特征进行手动质量控制调整并校正时区变化。在本文中，我们用参与者的数据说明了管道，每个参与者研究了200多天。基于活动描记法的睡眠持续时间测量与自我报告的睡眠质量评级相关。智能手机使用和GPS定位数据的同时测量支持睡眠时间推断的有效性，并揭示了睡眠时间的手机测量如何与体动记录数据不同。我们讨论了与其他可用的睡眠估计方法相关的DPSAs的使用，并提供了示例用例，包括多维，深度纵向表型，与精神疾病相关的动态扩展测量，以及结合可穿戴体动记录和个人电子设备数据（例如，智能手机和平板电脑）来测量健康和疾病中各种行为变化的个体差异的可能性。深度睡眠表型分析的新开源管道DPSlogic分析来自可穿戴设备的原始加速度计数据，并估计睡眠开始和偏移，同时允许手动质量控制调整。
Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward–sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.
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影响因子：
2.6
作者：
Fonareva I;Amen AM;Zajdel DP;Ellingson RM;Oken BS
通讯作者：
Oken BS
影响因子：
2.1
作者：
Barnett, Ian;Onnela, Jukka-Pekka
通讯作者：
Onnela, Jukka-Pekka
DOI：
10.3390/s16050646
发表时间：
2016-05-05
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Mantua J;Gravel N;Spencer RM
通讯作者：
Spencer RM
影响因子：
2.6
作者：
Kaplan KA;Hirshman J;Hernandez B;Stefanick ML;Hoffman AR;Redline S;Ancoli-Israel S;Stone K;Friedman L;Zeitzer JM;Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures SOF Research Groups
通讯作者：
Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures SOF Research Groups
DOI：
10.1123/jmpb.2018-0068
发表时间：
2019-12
期刊：
Journal for the measurement of physical behaviour
影响因子：
--
作者：
John D;Tang Q;Albinali F;Intille S
通讯作者：
Intille S