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瓦克索:一种自适应的统计方法,将运动分为睡眠和清醒状态

Austin Vandegriffe¹, V A Samaranayake¹, Matthew S Thimgan²

¹Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, Missouri United States of America.

|December 11, 2025

概括

一个新的算法,睡眠和清醒分类的瓦斯斯坦算法 (WACSAW),准确地区分睡眠和清醒状态和运动数据. 这种先进的方法改进了现有的行为测量系统,以更好地分析睡眠.

科学领域:

生物医学工程生物医学工程
睡眠科学睡眠科学
数据科学数据科学数据科学

背景情况:

可穿戴的行为计对于在自然环境中研究睡眠至关重要.
现有的算法难以准确,特别是在安静的清醒状态下.
硬件的进步使得高频数据收集能够进行复杂的分析.

研究的目的:

开发和验证一种新的统计算法,以使用可穿戴行为测量数据改进睡眠/清醒分类.
解决当前算法在准确识别行为状态方面的局限性,特别是在活动较低的时期.

主要方法:

开发了Wasserstein算法来分类睡眠和清醒状态 (WACSAW),利用最佳的运输技术.
WACSAW分析了运动变化和集群活动分布,使用k-最近的邻居.
分段被归类为睡眠或觉醒,基于接近理想的睡眠分布.

主要成果:

在对参与者日志进行验证后,WACSAW在分类睡眠和清醒状态方面取得了超过95%的整体准确性.
与临床验证的行为测量系统相比,该算法显示出大约10%的优异性能.
该方法提供了一个个性化调整的统计方法,以actimetry.

结论:

WACSAW提供了一种新,准确和个性调整的统计方法,用于从动度计数据中进行睡眠/清醒分类.
该算法改进了现有系统,特别是在具有挑战性的条件下,如安静的清醒状态.
该方法产生了可能与睡眠结果相关的辅助信息.

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