Timofejeva Inga, McCraty Rollin, Atkinson Mike, Joffe Roza, Vainoras Alfonsas, Alabdulgader Abdullah A, Ragulskis Minvydas
Department of Mathematical Modelling, Kaunas University of Technology, 51368 Kaunas, Lithuania.
HeartMath Institute, Boulder Creek, CA 95006, USA.
Int J Environ Res Public Health. 2017 Sep 1;14(9):998. doi: 10.3390/ijerph14090998.
A new analysis technique for the evaluation of the degree of synchronization between the physiological state of a group of people and changes in the Earth's magnetic field based on their cardiac inter-beat intervals was developed and validated. The new analysis method was then used to identify clusters of similar synchronization patterns in a group of 20 individuals over a two-week period. The algorithm for the identification of slow wave dynamics for every person was constructed in order to determine meaningful interrelationships between the participants and the local magnetic field data. The results support the hypothesis that the slow wave rhythms in heart rate variability can synchronize with changes in local magnetic field data, and that the degree of synchronization is affected by the quality of interpersonal relationships.
开发并验证了一种基于一组人的心跳间期来评估其生理状态与地磁场变化之间同步程度的新分析技术。然后,这种新的分析方法被用于在两周时间内识别20名个体中的相似同步模式集群。为了确定参与者与当地磁场数据之间有意义的相互关系,构建了针对每个人的慢波动力学识别算法。结果支持了这样的假设,即心率变异性中的慢波节律可以与当地磁场数据的变化同步,并且同步程度受人际关系质量的影响。