An improved multiscale distribution entropy for analyzing complexity of real-world signals

Deka, Bhabesh; Deka, Dipen

doi:10.1016/j.chaos.2022.112101

Cited by 13 publications

(9 citation statements)

References 34 publications

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“…In [14], Deka and Deka have used multiscale PEn (MPE) to examine the dynamical complexity of temporal structures in HRV signals of meditators and observed consistent results with that of [10]. Consistent results are also obtained from a recently developed improved multiscale distribution entropy measure [45]. It provides an interesting fact that actually complexity of HRV signal increases during meditation.…”

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

confidence: 68%

“…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%

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

confidence: 68%

Section: Nonlinear Parameters In Phase Spacementioning

confidence: 99%

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

Deka

2023

BioMed Eng OnLine

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“…At the beginning of the twenty-first century, more and more studies prove the need to study the nonlinear component of biological processes [8,9] to obtain, firstly, additional information about the transformation of biological processes due to reverse biological connections, to ensure the balance of functioning of the organism [10]. Secondly, they provide information about a sufficient number of internal resources to ensure physiological balance under the influence of destabilizing factors [11,12]. Thirdly, it is a valuable tool for analyzing the medical and biological indicators of population groups that have already undergone professional selection and their medical and biological parameters are within the normal range, but professional destabilizing factors can deplete the resources available in the body, and for their effective use, nonstandard approaches to assessment are required the level of regulation of the stability of the organism's functioning, taking into account their chaotic nature [13,14].…”

Section: Review Of the Literaturementioning

confidence: 99%

Intelligence Analysis of Empirical Data Based on Time Series

Ivanets

Khrashchevskyi

Kulik

et al. 2023

RIC

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Context. The problem of intelligent data analysis for assessing the stability of operators’ functioning as a component of safety management is considered.The object of the study was to verify estimates of the complexity and chaotic nature of physiological processes based on nonlinear dynamics methods. Objective. The goal of work is intelligent data analysis for assessing the stability of the functioning of a dynamic system based on the methods of non-linear dynamics. Method. Data intelligence to obtain additional useful information to avoid wrong decisions when deciding on the current state of the operator to be able to perform professional duties. Quantitative assessment of the complexity of physiological dynamics to determine the stability of feedback control processes of body subsystems and their constant adaptation to changes in environmental conditions. The presence of significant nonlinearities in the biomedical signals of the body is associated with the appearance of a chaotic component that describes the chaotic nature of the body’s processes. Due to the fact that biomedical signals have both a periodic and a chaotic component, the study of the latter makes it possible to determine the informational component of the nature of the internal organization of the organism and provide information about the possible destabilization of the functional state of the operator. The use of nonlinear dynamics methods to study changes in the operator’s body and provide additional independent prognostic information complementing traditional data analysis in the time and frequency domains. Several indices obtained by the methods of nonlinear dynamics are proposed, which contribute to the expansion of the diagnostic solution based on the available data. Results. The results of the study can be used during the construction of mathematical methods of non-linear dynamics to describe empirical data of this kind. Conclusions. Experimental studies have suggested recommending the use of non-linear methods dynamics as an an additional independent component that allows analyzing the chaotic component of biomedical signals to avoid wrong decisions during professional selection and assessment of the current state of aviation industry operators as one of the causes of adverse events in aviation. Prospects for further research may include the creation of a methodology based on nonlinear dynamics methods that will allow to increase the reliability of predicting a malfunction of the cardiovascular system as an indicator of a change in the balance of the functional state of the operator based on additional informative parameters, which can be used to assess triggers that may cause an adverse event in aviation, as well as an experimental study of the proposed mathematical approaches for a wide range of diagnostic problems.

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“…Unreliable entropy values arising with shortened time series are mainly caused by the existence of the threshold parameter of SE. In order to overcome the sensitivity for predefined parameters of the MSE, several multiscale based entropy methods have been introduced [23], [24], [25], [26], [27]. Among them, the multiscale dispersion entropy (MDE) gained popularity due to its ability in biomedical applications [28].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Kim,

Choi

2024

IEEE Access

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Heart rate variability (HRV), which is the variation of inter-beat intervals, exhibits complex characteristics on multiple temporal scales due to the balancing function of the autonomic nervous system. Although there are various nonlinear analysis methods for assessing the complexity of HRV, quantifying HRV over multiple scales is lacking. Here, we present a novel multiscale fuzzy dispersion entropy (MFDE) measure that incorporates quantifying fuzzy dispersion entropy over multiple temporal scales. The proposed MFDE comprises two steps: First, a coarse-graining procedure is carried out for the multiscale decomposition of an inter-beat interval. Second, it conducts FDE computation for each coarse-grained time series. It results in the quantification of complexity, reflecting the long-range correlations inherent in HRV. Using synthetic signals and actual electrocardiogram (ECG), we evaluate the performance of MFDE and compare it to the traditional multiscale entropy methods. The results using synthetic signals show better robustness of MFDE for quantifying complexity with various lengths and predefined parameters. The results using ECGs demonstrate that the proposed MFDE leads to more significant discrimination of HRVs of different cardiovascular states regarding p-values from the Mann-Whitney U test. The capability of MFDE can provide a prospective tool for real-time and practical computer-aided diagnosis using HRV analysis.INDEX TERMS Heart rate variability, RR intervals, Complexity, Multiscale fuzzy dispersion entropy.

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An improved multiscale distribution entropy for analyzing complexity of real-world signals

Cited by 13 publications

References 34 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

Intelligence Analysis of Empirical Data Based on Time Series

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

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