Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing.

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第一作者： Daniel M,Roberts

第一单位： Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA; Proactive Life, Inc, New York, New York, USA. Electronic address: danmroberts@gmail.com.

作者： Daniel M,Roberts ^[1] ; Margeaux M,Schade ^[2] ; Lindsay,Master ^[2] ; Vasant G,Honavar ^[3] ; Nicole G,Nahmod ^[2] ; Anne-Marie,Chang ^[2] ; Daniel,Gartenberg ^[4] ; Orfeu M,Buxton ^[2]

作者单位： Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA; Proactive Life, Inc, New York, New York, USA. Electronic address: danmroberts@gmail.com. ^[1] Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA. ^[2] Faculty of Data Sciences, College of Information Science and Technology, The Pennsylvania State University, University Park, Pennsylvania, USA. ^[3] Proactive Life, Inc, New York, New York, USA. ^[4]

关键词 24-hour Actigraphy Classification Machine learning Wearables

主题词成年人(Adult);人类(Humans);体动描记术(Actigraphy);睡眠(Sleep);多道睡眠描记术(Polysomnography);休息(Rest);动脉粥样硬化(Atherosclerosis)

DOI 10.1016/j.sleh.2023.07.001

PMID 37573208

发布时间 2024-10-03

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Sleep health

2023年9卷5期

596-610页

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Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing.

Sleep health

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Sleep health

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Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing.

Sleep health

相似文献

Sleep health

相关期刊

检索历史清除