复杂性匹配的数学表征在治疗循环冥想期间的复杂性匹配
Naseha Wafa Qammar1, Minvydas Ragulskis1, Roza Joffe-Luiniene2
1Kaunas University of Technology, Kaunas, Lithuania.
Nonlinear dynamics, psychology, and life sciences
|July 10, 2023
概括
这项研究使用了一种新的H级算法来测量冥想期间治疗者和治疗者之间的心率变化 (HRV) 的复杂性匹配. 在治愈过程中,H-rank算法成功地记录了HRV复杂性的微妙变化.
科学领域:
- 心脏病学 心脏病学
- 复杂性科学 复杂性科学
- 生物物理学的生物物理.
背景情况:
- 心率变化 (HRV) 分析对于理解生理状态至关重要.
- 复杂性匹配是一种理论概念,表明相互连接的系统具有相似的复杂性.
- 冥想和治疗实践可能会影响生理同步.
研究的目的:
- 在冥想协议期间评估治疗师和受治疗者的HRV之间的复杂性匹配.
- 应用一种新的数学方法,H-rank算法,用于评估HRV复杂性.
- 为了确定H级算法是否能够在治愈相互作用期间检测到微妙的变化.
主要方法:
- 在75分钟的心脏集中冥想期间,对8名治疗师和1名受治疗者进行了一项实验.
- 记录了高分辨率HRV信号与同步的内部时钟.
- 采用汉克尔变换 (H-rank) 方法来重建时间序列和测量代数复杂性.
- 利用嵌入吸引器技术来实现H级的状态空间可视化.
主要成果:
- 在冥想治愈阶段,治疗师和受治疗者之间的重建H级复杂度的变化.
- H-rank算法成功地记录了HRV复杂性的微妙变化.
- 观察到治疗师和治疗者的HRV信号之间的复杂性匹配.
结论:
- 通过HRV复杂性匹配,H级算法能够通过HRV复杂性匹配记录愈合过程中的微妙变化.
- 在冥想期间观察到治疗师和治疗者的HRV之间的复杂性匹配.
- 需要进一步的研究来探索HRV匹配的潜在机制.
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