Activation of Sympathetic Nervous System as a Biomarker for Deep Meditation

Guo, Menglin; Guo, Xiaoli; Wang, Meiyun; Wang, Xu; Xue, Ting; Wang, Zhuo; Li, Han; Xu, Tianjiao; He, Bin; Cui, Donghong; Tong, Shanbao

doi:10.1109/ner.2019.8717006

Cited by 3 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is observed that during meditation, a majority of researchers demonstrate significant decrease in the complexity of HRV signal by computing nonlinear parameters (entropy, CD, MED, LLE, Poincaré indices, etc.) [12,13,24,26,27,29,33], while a few have observed an increase in dynamical complexity [15,28,39]. To examine the regularity and complexity of HRV signals, various forms of entropy parameters are proposed.…”

Section: Discussionmentioning

confidence: 99%

“…13, where there is a scaling crossover indicating the effect of meditation. In (23) [39] have found that both short-term and long-term scaling exponents have significantly increased. In another work, Kamath [15] observes decrement in HFD, which is interpreted as the increase of LFp owing to the shift of the RSA by parasympathetic mediation.…”

Section: Application Of Recurrence Plot and Recurrence Quantification...mentioning

confidence: 98%

See 1 more Smart Citation

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

Deka

2023

BioMed Eng OnLine

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Application Of Recurrence Plot and Recurrence Quantification...mentioning

confidence: 98%

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

Deka

2023

BioMed Eng OnLine

View full text Add to dashboard Cite

show abstract

“…The results show significantly lower FD values (p <0.05) during meditation. However, Guo et al [8] have found significantly higher scaling exponents (both short-term and long-term) during deep meditation. Furthermore, driven by the fact that fractal behavior during meditation may change from segment to segment, Song et al [28] have employed multifractal analysis.…”

Section: Introductionmentioning

confidence: 96%

“…However, results are found to be contrasting. Some show a stimulation of the SNS [8], [9], while others observe the stimulation of the PNS during meditation [10], [11]. Thus, there is a need of further investigation of the HRV during meditation.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, frequency domain parameters quantify the spectral power distribution; predominantly over very low frequency (VLF), low frequency (LF), and high frequency (HF) bands. The power measurements and the ratio of LF to HF power is used in many studies [8]- [11]. However, wavelet-based approaches are used only in a few works for studying the meditative HRV signal [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers

Deka

2023

IEEE Access

View full text Add to dashboard Cite

In this paper, we propose a textural information-based analysis of scalogram image obtained from continuous wavelet transform of heart rate variability (HRV) signal to study its dynamics during meditation. In addition to features from scalogram image, visibility graph-based complexity measures and multiscale permutation entropies (MPEs) from HRV signal are used to elucidate the modulation in autonomic activity of heart during meditative and non-meditative state. Significant changes in the probability distribution of pixel intensities of scalogram image and undiminished permutation entropy at higher scales are observed during the meditative state. From the extracted features, we have selected the top-ranked features based on ReliefF algorithm and minimum threshold weight of importance set at 0.04. Considering the small sample size (12 subjects) of meditation dataset, we have employed a novel data augmentation technique based on averaging of feature sets to overcome the issue of overfitting. To examine the efficacy of the proposed technique, both the non-augmented and augmented data are applied to four different types of classifiers, namely k-nearest neighbor, support vector machine (SVM), logistic regression and random forest classifiers. Experimental results demonstrate that performance of classifiers in distinguishing meditative and pre-meditative state are much superior with the augmented data as compared to that with regular non-augmented data. Out of these classifiers, radial basis function (RBF)-based SVM results in the best performance with an average accuracy of 96.67%, sensitivity of 95.83% and specificity of 97.5%.

show abstract