使用格兰杰因果关系和基于得分的结构学习的生理睡眠数据的因果分析
Alex Thomas1, Mahesan Niranjan1, Julian Legg2
1School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究使用格兰杰因果关系和DYNOTEARS来分析600名成年人的睡眠研究数据. 研究结果表明,身体形状在睡眠期间影响心脑连接,方法结果各不相同.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 睡眠研究产生复杂的时间序列传感器数据,对医学理解至关重要.
- 对个性化医疗来说,从多睡眠学数据中推断因果关系至关重要.
研究的目的:
- 通过使用格兰杰因果关系和DYNOTEARS. 来从多梦图数据中学习因果结构.
- 为了比较这两种因果发现方法的有效性和结果.
- 探索因果结构和参与者特征之间的关系.
主要方法:
- 应用格兰杰因果关系和DYNOTEARS对600名成年志愿者的多梦图数据.
- 学习了动态贝叶斯网络 (DBN) 来表示因果关系.
- 将图形结构进行比较,并分析与参与者人体测量的关联.
主要成果:
- 两种方法都发现了相似之处,包括电眼图 (EOG) 和电脑图 (EEG) 信号之间的相互因果关系,以及睡眠位置和血液氧和 (SpO2) 之间的相似之处.
- DYNOTEARS揭示了更多与睡眠位置的因果关系,而不是格兰杰因果关系.
- 参与者的腰部大小与心电图 (ECG) 和EOG/EEG信号之间的因果关系有关.
结论:
- 身体形状可能会影响睡眠期间的心脑关系.
- 格兰杰因果关系和DYNOTEARS可以从现实世界的睡眠数据中产生不同的因果结构.
- 因果发现方法为睡眠期间的生理相互作用提供了洞察力.
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