同步的几何学:通过使用系统间的复发网络量化个人之间的压力生理信号的合方向
Fred Hasselman1, Luciënne den Uil2,3, Renske Koordeman2
1Behavioural Science Institute, Radboud University, Nijmegen, Netherlands.
Frontiers in network physiology
|November 29, 2023
概括
系统间复杂性网络 (IRN) 显示出分析复杂生理数据中的同步动态的潜力. 虽然IRN可以建议交互方向,但区分双向或无合需要进一步研究.
科学领域:
- 复杂系统分析 复杂系统分析
- 生理学数据科学生理学数据科学
- 网络理论的应用 网络理论的应用
背景情况:
- 在相互作用系统中估计合强度和方向具有挑战性,没有普遍最佳的方法.
- 系统间递归网络 (IRN) 提供了基于图的方法,将自动和交叉递归结合起来,将交叉递归量化分析 (CRQA) 扩展到多个系统.
研究的目的:
- 研究IRN对于分析生理变量之间的合动态的实用性.
- 评估IRN在确定严重到严重智力障碍 (SPID) 的客户及其照顾者之间的互动方向方面的能力.
主要方法:
- 从五个生理变量 (加速,血压,皮质活动,心率,皮肤温度) 构建多变量时间序列.
- 应用的系统间反复性网络 (IRN) 框架,使用交叉聚类系数推断合方向.
- 分析了在住院护理环境中客户-护理人员二元体内的复杂相互作用.
主要成果:
- IRN分析提供了客户和护理人员之间潜在的合方向的见解.
- 交叉聚类系数表明了相互作用的潜在驱动因素.
- 确定双向合与没有合仍然是一个挑战.
- 完整的IRN揭示了合生理过程中的潜在反循环.
结论:
- IRN是探索多变量生理数据中的同步动态的一个有希望的方法.
- 需要进一步开发方法,才能最终区分双向合和无合.
- 需要进行额外的研究才能充分阐明跨不同层次相互作用的合动态.
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