将异质性纳入混合隐藏马尔科夫模型,并应用于睡眠-觉醒周期
Jordan Aron1, Paul S Albert1, Mark B Fiecas2
1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
Statistics in medicine
|September 5, 2025
概括
一个新的混合隐藏马尔科夫模型 (HMM) 改进了体力活动监测器的睡眠-清醒周期分析,特别是使用较短的数据. 它解释了个人的差异,
科学领域:
- 时间生物学
- 生物统计学
- 公共卫生
背景情况:
- 睡眠周期对整体健康至关重要.
- 个人体育活动监测器 (PAM) 提供睡眠模式的洞察力.
- 隐藏的马尔科夫模型 (HMM) 是有效的无监督方法来分析活动数据.
研究的目的:
- 开发和评估一个混合的HMM,考虑到个人在体育活动中的异质性.
- 将混合HMM与标准HMM的精度进行比较.
- 将HMM应用于睡眠总结统计的现实世界动画数据.
主要方法:
- 开发了一个混合的隐藏马尔科夫模型 (HMM) 来解决个体活动的差异.
- 进行了广泛的模拟,以将混合HMM与标准HMM精度进行比较.
- 将两个HMM应用于国家健康和营养检查调查 (NHANES) 的动图数据.
主要成果:
- 混合HMM的准确性高于标准HMM,后续时间较短.
- 这两种模型的准确性都相似,重复测量大量,更有利于标准HMM的速度.
- 对NHANES数据的分析显示了睡眠总结统计数据,与BMI和年龄相关活动异质性相关的差异.
结论:
- 混合HMM为睡眠-清醒周期分析提供了更高的准确性,特别是在数据有限的场景中.
- 身体活动的个体异质性受到BMI和年龄等因素的影响, 影响睡眠模式推断.
- 这项研究强调了先进的统计建模对于从PAM中提取有意义的健康信息的有用性.
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