Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Abstract

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram

“…Its inverse correlation with the spectral power in the low frequencies led the authors to hypothesize that a meditation feature would be its lower complexity because practitioners engage in less distraction during the practice. Gao et al ( 2016 ), Sik et al ( 2017 ) also reported decrease in complexity related to CDM meditation. They combined both the Wavelets Transform with the complexity and source analysis, to analyze the MBSR program.…”

“…In the meditation field, multimodal approaches are seen since the first studies appearing in the 70's, which aimed at incorporating complementary information from peripheral physiology, like skin resistance, heart rate and respiration (Wallace, 1970 ; Wallace et al, 1971 ; Elson et al, 1977 ; Fenwick et al, 1977 ; Hebert and Lehmann, 1977 ; Corby et al, 1978 ). Recently, new analyses have combined these signals (Gao et al, 2016 ; Chandra et al, 2017 ; Sik et al, 2017 ; Jiang et al, 2020 ).…”

Acute Effects of Various Movement Noise in Differential Learning of Rope Skipping on Brain and Heart Recovery Analyzed by Means of Multiscale Fuzzy Measure Entropy

“…However, the explanatory power of an interaction analysis via correlation of different analysis parameters based even on different biosignals, as conducted in John et al (2020), has to be handled with caution. Investigating an interaction between brain and heart activity proves problematic due to various signal content between the (neuro-)physiological systems (Sik et al, 2017). Furthermore, different data preprocessing, e.g., bandpass and artifact filtering, as well as calculation techniques, e.g., time-versus frequency domain, dependent on the respective brain or heart measure aggravates besides varying value ranges of these parameters an interference-free interaction analysis.…”

“…The most popular approaches for analyzing nonlinear signal characteristics investigate signal irregularity based on the entropy theory (Shannon, 1948). Commonly, irregularity of the EEG is characterized as signal noise, whereas it may contain valuable information (Sik et al, 2017) and seems relevant for a healthy, efficient and flexible neural function (Kosciessa et al, 2020). Regarding ECG, it is well known that the heart activity (HRV) needs to contain some noise or irregularity for being healthy (Peng et al, 1995).…”

“…Considering the analysis of the interaction between brain and heart activity, only studies correlating brain and heart entropies with no relation to PA were found. A study (Sik et al, 2017) investigated a relation of the (neuro-)physiological systems comparing a mental exercise with a resting condition. A linear correlation between brain and heart rate wavelet entropy was found during the mental exercise in contrast to the resting condition, particularly in central brain regions.…”