Physiological fractals: visual and statistical evidence across timescales and experimental states.

Heart-rate variability (HRV) increases during compassion meditation, but the best timescale for measuring it and how the signal's stability changes over time remain unclear. Applying advanced time-series analyses to data from a two-week compassionate mind training intervention, the authors found that the pattern of HRV correlations across resting and meditation states was similar across multiple recording timescales, indicating a fractal-like property. After training, the HRV signal during compassion meditation became more highly correlated and less stochastic (variable) than before training, and the average difference between rest and meditation states decreased. These results offer new visual and statistical markers of HRV change across experimental states.

Study at a glance

Abstract

A marker of engaging in compassion meditation and related processes is an increase in heart-rate variability (HRV), typically interpreted as a marker of parasympathetic nervous system response. While insightful, open questions remain. For example, which timescale is best to examine the effects of meditation and related practices on HRV? Furthermore, how might advanced time-series analyses--such as stationarity--be able to examine dynamic changes in the mean and variance of the HRV signal across time? Here we apply such methods to previously published data, which measured HRV pre- and post- a two-week compassionate mind training (CMT) intervention. Inspection of these data reveals that a visualization of HRV correlations across resting and compassion meditation states, pre- and post-two-week training, is retained across numerous recording timescales. Here, the fractal-like nature of our data indicates that the accuracy of representing HRV data can exist across timescales, albeit with greater or lesser granularity. Interestingly, inspection of the HRV signal at Time 2 compassion meditation versus Time 1 revealed a more highly correlated (i.e. potentially more stable) signal. We followed up these results with tests of stationarity, which revealed Time 2 had a less stochastic (variable) signal than Time 1, and a measure of distance in the time series, which showed that Time 2 had less of an average difference between rest and meditation than at Time 1. Our results provide novel assessment of visual and statistical markers of HRV change across distinct experimental states.