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Register a new userEffect of meditation on scaling behavior and complexity of human heart rate variability
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The heart beat data recorded from samples before and during meditation are analyzed using two different scaling analysis methods. These analyses revealed that mediation severely affects the long range correlation of heart beat of a normal heart. Moreover, it is found that meditation induces periodic behavior in the heart beat. The complexity of the heart rate variability is quantified using multiscale entropy analysis and recurrence analysis. The complexity of the heart beat during mediation is found to be more.
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Characterisations of the long-term behaviour of heart rate variability in humans have emerged in the last few years as promising candidates to became clinically significant tools. We present two different statistical analyses of long time recordings of the heart rate variation in the Eastern Oyster. The circulatory system of this marine mollusk has important anatomical and physiological dissimilitudes in comparison to that of humans and it is exposed to dramatically different environmental influences. Our results resemble those previously obtained in humans. This suggests that in spite of the discrepancies, the mechanisms of long--term cardiac control on both systems share a common underlying dynamic.
In this work we study the characteristics of the heart rate variability (HRV) as a function of age and gender. The analyzed data include previous results reported in the literature. The data obtained in this work expand the range of age studied until now revealing new behaviors not reported before. We analyze some measurements in the time domain,in the frequency domain and nonlinear measurements. We report scaling behaviors and abrupt changes in some measurements. There is also a progressive decrease in the dimensionality of the dynamic system governing the HRV, with the increase in age that is interpreted in terms ofautonomic regulation of cardiac activity.
The knowledge of transitions between regular, laminar or chaotic behavior is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods which however require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart rate variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e. chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our new measures to the heart rate variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.
Recent empirical observations suggest a heterogeneous nature of human activities. The heavy-tailed inter-event time distribution at population level is well accepted, while whether the individual acts in a heterogeneous way is still under debate. Motivated by the impact of temporal heterogeneity of human activities on epidemic spreading, this paper studies the susceptible-infected model on a fully mixed population, where each individual acts in a completely homogeneous way but different individuals have different mean activities. Extensive simulations show that the heterogeneity of activities at population level remarkably affects the speed of spreading, even though each individual behaves regularly. Further more, the spreading speed of this model is more sensitive to the change of system heterogeneity compared with the model consisted of individuals acting with heavy-tailed inter-event time distribution. This work refines our understanding of the impact of heterogeneous human activities on epidemic spreading.
Detrended fluctuation analysis (DFA) is a scaling analysis method used to quantify long-range power-law correlations in signals. Many physical and biological signals are ``noisy, heterogeneous and exhibit different types of nonstationarities, which can affect the correlation properties of these signals. We systematically study the effects of three types of nonstationarities often encountered in real data. Specifically, we consider nonstationary sequences formed in three ways: (i) stitching together segments of data obtained from discontinuous experimental recordings, or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts -- a ``cutting procedure commonly used in preparing data prior to signal analysis; (ii) adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitude, and (iii) generating a signal comprised of segments with different properties -- e.g. different standard deviations or different correlation exponents. We compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities.
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