Multifractal analysis of heartbeat dynamics during meditation training

Song, Renliang; Bian, Chunhua; D.Y., Qianli

doi:10.1016/j.physa.2012.11.050

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…Heart Rate Variability (HRV) data from patients with liver cirrhosis: Decreased HRV holds prognostic significance in chronic liver disease (Bhogal et al, 2018; Mani et al, 2009). A variety of different methods have been developed for complexity analysis of HRV including multiscale entropy and multifractal analysis (Peng et al, 1995; Costa et al, 2005; Zheng et al, 2010; Bian et al, 2012; Jiang et al, 2013; Song et al, 2013), however, recent studies revealed that the Poincaré plot provides better prognostic value in patients with liver disease in comparison with entropy and fractal indices of HRV (Bhogal et al, 2018). In this study, HRV is analyzed using the extended Poincaré plot to determine if the greater correlation between sequential inter-beat intervals predict mortality for non-invasive patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

et al. 2019

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The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterize the intrinsic behavior of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson’s correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic (ROC) curve provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma.

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Section: Introductionmentioning

confidence: 99%

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

et al. 2019

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“…For instance, even though the database has been frequently used in the recent scientific researches, [11,15,25] more data should be examined to generalize the findings. Further investigations can employ more varied features and other meditation techniques.…”

Section: Discussionmentioning

confidence: 99%

Poincare indices for analyzing meditative heart rate signals

Goshvarpour¹,

Goshvarpour²

2015

Biomed J

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Background: Poincare plots are commonly used to study the nonlinear behavior of physiologic signals. The aim of this study is to evaluate the Poincare plot indices of human heart rate signals during meditation. Methods:For this purpose, heart rate time series of eight Chi meditators available in Physionet database were used. Poincare plots with lags of 1 and 6 were constructed, and the ratio of the minor axis to major axis (SD1/SD2) and the area of Poincare plots were calculated for each lag. Results:The results show that the SD1/SD2 ratio increased significantly during meditation compared to that before meditation, especially the index measured from Poincare plots reconstructed with a lag of 6 (p < 0.05). In addition, in both lags, the area of Poincare plots decreased significantly during meditation compared to before meditation (p < 0.05). Conclusion:The comparative dynamic measures of the Poincare plot indices during and before meditation give more insight of the heart rate signals in a specific psychophysiological state. (Biomed J 2015;38:229-234)

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“…Consequently, the state of the dynamical system at the time moment n can be represented. Thus we can use the recurrence matrix for expression of the RP: (2) where N is the points number of vector i X , || || is the norm for evaluating the distance between the two vectors, and the is a threshold, which can determine whether or not the two vectors are close to each other. Finally, () is the Heaviside function (if x<0, the (x)=0, else the (x)=1).…”

Section: A Recurrence Quantification Analysismentioning

confidence: 99%

“…This variability is affected by many factors, and under the regulation of autonomic nervous (sympathetic and parasympathetic nervous) system. Consequently, the autonomic nervous related physiological and pathological states of the cardiac system can be reflected by HRV [1,2]. The heart is a complex nonlinear dynamical system, which determined the high complexity of HRV signals generated by the cardiac system.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale recurrence quantification analysis of heartbeat interval series in healthy vs. Heart failure subjects

Huo

Huang

et al. 2014

2014 7th International Conference on Biomedical Engineering and Informatics

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Recurrence plot is a useful analysis method for nonlinear time series, and has been widely used in studies of heart rate variability in recent years. In this paper, recurrence plot and corresponding quantification analysis were utilized to analyze the heart rate variability data from healthy people and congestive heart failure sufferers. It was found that the measures for standard recurrence quantification analysis failed to distinguish between the two groups. Therefore, a multi-scale recurrence quantification analysis method was proposed, which is able to implement the quantitative analysis for recurrence structures of coarse-grained heartbeat interval series on multiple time scales. Experiment results showed that, on certain time scales, the recurrence structures of abnormal heart rate fluctuation from subjects with congestive heart failure could be reflected more clearly by multi-scale RQA parameters.

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Multifractal analysis of heartbeat dynamics during meditation training

Cited by 19 publications

References 19 publications

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

Poincare indices for analyzing meditative heart rate signals

Multi-scale recurrence quantification analysis of heartbeat interval series in healthy vs. Heart failure subjects

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