Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective

Abstract

Introduction: In recent times, an upsurge in the investigation related to the effects of meditation in reconditioning various cardiovascular and psychological disorders is seen. In majority of these studies, heart rate variability (HRV) signal is used, probably for its ease of acquisition and low cost. Although understanding the dynamical complexity of HRV is not an easy task, the advances in nonlinear analysis has significantly helped in analyzing the impact of meditation of heart regulations. In this review, we intend to present the various nonlinear approaches, scientific findings and their limitations to develop deeper insights to carry out further research on this topic.

Results: Literature have shown that research focus on nonlinear domain is mainly concentrated on assessing predictability, fractality, and entropy-based dynamical complexity of HRV signal. Although there were some conflicting results, most of the studies observed a reduced dynamical complexity, reduced fractal dimension, and decimated long-range correlation behavior during meditation. However, techniques, such as multiscale entropy (MSE) and multifractal analysis (MFA) of HRV can be more effective in analyzing non-stationary HRV signal, which were hardly used in the existing research works on meditation.

Conclusions: After going through the literature, it is realized that there is a requirement of a more rigorous research to get consistent and new findings about the changes in HRV dynamics due to the practice of meditation. The lack of adequate standard open access database is a concern in drawing statistically reliable results. Albeit, data augmentation technique is an alternative option to deal with this problem, data from adequate number of subjects can be more effective. Multiscale entropy analysis is scantily employed in studying the effect of meditation, which probably need more attention along with multifractal analysis.

Methods: Scientific databases, namely PubMed, Google Scholar, Web of Science, Scopus were searched to obtain the literature on "HRV analysis during meditation by nonlinear methods". Following an exclusion criteria, 26 articles were selected to carry out this scientific analysis.

Keywords: Dynamical complexity; Entropy; Heart rate variability; Long-range correlation; Meditation; Phase-space representation.

Fig. 1

HRV signals of a representative Kundalini Yoga meditator (Y1) before meditation (left) and during meditation (right) [14]

Fig. 2

A trend of publications related to HRV analysis for meditation

Fig. 3

Pie diagram representation of the distribution of meditation techniques used in research articles

Fig. 4

Poincaré plot of HRV signal before meditation (left) and during meditation (right) [42]

Fig. 5

Variation of the E2(m) against embedding dimensions to determine MED for HRV time series corresponding to meditative and pre-meditative states using Cao’s method [50]

Fig. 6

Variation of percentage of FNNs against embedding dimensions to obtain MED for HRV time series corresponding to meditative and pre-meditative states using Kennel’s method [52]

Fig. 7

Variation of mutual information against varying time delay for HRV time series corresponding to meditative and pre-meditative states

Fig. 8

CD values of HRV time series after embedding the series with varying embedding dimensions m for meditative and pre-meditative states [24]

Fig. 9

LLE for HRV time series corresponding to meditative and pre-meditative states [24]

Fig. 10

Recurrence plot of representative HRV time series before meditation [10]

Fig. 11

Recurrence plot of representative HRV time series during meditation [10]

Fig. 12

Error bar plot of DEA for Chi meditators’ HRV time series during and before meditation [, Fig. 3]

Fig. 13

DFA of representative instantaneous HRV time series during and before meditation [, Fig. 3]

Fig. 14

Representation of visibility graph of an HRV time series

Fig. 15

The degree distribution, P(k) of visibility graph network for HRV time series of meditators [30]

Fig. 16

Bispectrum plot for HRV signal during pre-meditative state [34]

Fig. 17

Bispectrum plot for HRV signal during meditative state [34]

Fig. 18

Bicoherence plot for HRV signal during pre-meditative state

Fig. 19

Bicoherence plot for HRV signal during meditative state

Fig. 20

Criteria adopted for article selection