Visibility graph analysis on heartbeat dynamics of meditation training

Jiang, Sen; Bian, Chunhua; Ning, Xinbao; Qianli, D. Y.

doi:10.1063/1.4812645

Cited by 51 publications

(47 citation statements)

References 23 publications

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“…Theoretical research on visibility graphs has elaborated on mathematical methods [8][9][10][11] and some rigorous results on the properties of these graphs when associated to canonical models of complex dynamics have been obtained [12][13][14][15]. From a practical point of view, this method has been used as a feature extraction procedure to construct feature vectors from time series for statistical learning purposes (see [16][17][18][19][20][21][22] for just a few examples in the life sciences or [23][24][25][26][27][28][29][30] for other applications in the physical sciences). Very recently [31], this paradigm has been theoretically extended to handle scalar fields.…”

Section: Introductionmentioning

confidence: 99%

Visibility Graphs for Image Processing

Iacovacci

Lacasa

2020

IEEE Trans. Pattern Anal. Mach. Intell.

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The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters and compressors. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.

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Section: Introductionmentioning

confidence: 99%

Visibility Graphs for Image Processing

Iacovacci

Lacasa

2020

IEEE Trans. Pattern Anal. Mach. Intell.

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“…We have divided the total number of events in three ranges of n g . In doing so, although the number of events in some is comparatively low but this does not affect the result of the analysis as we have highlighted earlier in the text that this new method only can deliver reliable results with short data even with 400 data points [32].…”

Section: Our Methods Of Analysismentioning

confidence: 88%

Pion Fluctuation in High-energy Collisions --- A Chaos-based Quantitative Estimation with Visibility Graph Technique

Bhaduri¹,

Ghosh²

2017

Acta Phys. Pol. B

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We propose a new approach for studying pion fluctuation for deeper understanding of the dynamical process involved, from a perspective of fBm-based complex network analysis method called Visibility graph Analysis. This chaos-based, rigorous, non-linear technique is applied to study the erratic behavior of multipion production in π − -Ag/Br interactions at 350 GeV. This method can offer reliable results with finite data points. The Power of Scale-freeness of Visibility Graph denoted by-PSVG is a measure of fractality, which can be used as a quantitative parameter for the assessment of the state of a chaotic system. The event-wise fluctuation of the multipion production process can be described by this parameter-PSVG. From the analysis of the PSVG parameter, we can quantitatively confirm that fractal behavior of the particle production process depends on the target excitation and also the fractality decreases with the increase of target excitation.

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“…Hence periodic series is transformed to a regular graph, random series to a random graph and naturally fractal series to a scale-free network in which the graph's degree distribution conforms to the power-law with respect to its degree. Thus a fractal series can be mapped into a scale-free visibility graph [29], that too from a series with a finite number of points [40] as against the other non-stationary, nonlinear methods like DFA, MF-DFA which require a infinite data series as input.…”

Section: Methods Of Analysismentioning

confidence: 99%

Azimuthal Anisotropy in High-Energy Nuclear Collision: An Approach Based on Complex Network Analysis

Bhaduri

Ghosh

2018

Advances in High Energy Physics

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Recently, a complex network based method of visibility graph has been applied to confirm the scale-freeness and presence of fractal properties in the process of multiplicity fluctuation. Analysis of data obtained from experiments on hadron-nucleus and nucleusnucleus interactions results in values of Power of Scale-Freeness of Visibility Graph (PSVG) parameter extracted from the visibility graphs. Here, the relativistic nucleus-nucleus interaction data have been analysed to detect azimuthal anisotropy by extending the visibility graph method and extracting the average clustering coefficient, one of the important topological parameters, from the graph. Azimuthal-distributions corresponding to different pseudorapidity regions around the central pseudorapidity value are analysed utilising the parameter. Here we attempt to correlate the conventional physical significance of this coefficient with respect to complex network systems, with some basic notions of particle production phenomenology, like clustering and correlation. Earlier methods for detecting anisotropy in azimuthal distribution were mostly based on the analysis of statistical fluctuation. In this work, we have attempted to find deterministic information on the anisotropy in azimuthal distribution by means of precise determination of topological parameter from a complex network perspective.

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Visibility graph analysis on heartbeat dynamics of meditation training

Cited by 51 publications

References 23 publications

Visibility Graphs for Image Processing

Visibility Graphs for Image Processing

Pion Fluctuation in High-energy Collisions --- A Chaos-based Quantitative Estimation with Visibility Graph Technique

Azimuthal Anisotropy in High-Energy Nuclear Collision: An Approach Based on Complex Network Analysis

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