Investigation on HRV Signal Dynamics for Meditative Intervention

Deka, Dipen; Deka, Bhabesh

doi:10.1007/978-981-15-4032-5_89

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Phase-space transformation is found to be the basis for various types of nonlinear analyses, since it provides the scope to analyze a 1-D time series under higher dimensions. This led to its use in studying the effects of meditation from the aspects of asymptotic stability [24,27,55], time correlation of phase trajectories [10,27,55], dynamical complexity [10,14,26,37,41,45], etc. Nevertheless, the computation of MED from phase space representations using FNN and Cao's techniques (shown above) have revealed that HRV signals under meditative and pre-meditative conditions can be best studied within MED=3.…”

Section: Nonlinear Parameters In Phase Spacementioning

confidence: 99%

“…Each element of these vectors ( y n ) are mapped onto a word w n,k for k = 1,..,m having two letters, say s n,k and q n,k corresponding to sign and symbolic increment, respectively. These m words ( w n,k ) in a vector, form a full word denoted by w n [41]:…”

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

confidence: 99%

“…The increment entropy, H(m) for a resolution level (r) is given by: Deka and Deka [41] have observed reduced IncrEn value especially during KYM and marginally lower entropy during Chi meditation than that before meditation, though the results are not statistically significant. This indicates that there may be a reduced irregularity and dynamical complexity during meditation.…”

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

confidence: 99%

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

Deka

2023

BioMed Eng OnLine

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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.

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Section: Nonlinear Parameters In Phase Spacementioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

Deka

2023

BioMed Eng OnLine

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

Deka

2023

IEEE Access

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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%.

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Investigation on HRV Signal Dynamics for Meditative Intervention

Cited by 2 publications

References 13 publications

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

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

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

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