Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis

2022 ◽  
Vol 155 ◽  
pp. 111736
Author(s):  
Huizi Cui ◽  
Lingge Zhou ◽  
Yan Li ◽  
Bingyi Kang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interbeat Interval ◽  
Interval Time ◽  
Series Analysis ◽  
Interval Time Series ◽  
Belief Entropy

scholarly journals Supplemental Material: Centennial to millennial variability of greenhouse climate across the mid-Cenomanian event

2021 ◽  
Author(s):  
Chao Ma ◽  
et al.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interval Time ◽  
Greenhouse Climate ◽  
Series Analysis ◽  
Interval Time Series

Background of the studied interval, time series analysis of the grayscale of the marls, micropaleontology, and supplemental figures and tables.

scholarly journals Supplemental Material: Centennial to millennial variability of greenhouse climate across the mid-Cenomanian event

2021 ◽  
Author(s):  
Chao Ma ◽  
et al.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interval Time ◽  
Greenhouse Climate ◽  
Series Analysis ◽  
Interval Time Series

Background of the studied interval, time series analysis of the grayscale of the marls, micropaleontology, and supplemental figures and tables.

scholarly journals Classification of Interbeat Interval Time-series Using Attention Entropy

2020 ◽  
pp. 1-1
Author(s):  
Jiawei Yang ◽  
Gulraiz Iqbal Choudhary ◽  
Susanto Rahardja ◽  
Pasi Franti
Keyword(s):  
Time Series ◽  
Interbeat Interval ◽  
Interval Time ◽  
Interval Time Series

scholarly journals Analysing the Dynamics of Interbeat Interval Time Series Using Grouped Horizontal Visibility Graph

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9926-9934 ◽  
Author(s):  
Gulraiz Iqbal Choudhary ◽  
Wajid Aziz ◽  
Ishtiaq Rasool Khan ◽  
Susanto Rahardja ◽  
Pasi Franti
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Interbeat Interval ◽  
Horizontal Visibility ◽  
Interval Time ◽  
Interval Time Series ◽  
Horizontal Visibility Graph

scholarly journals Multiscale ordinal network analysis of human cardiac dynamics

2017 ◽  
Vol 375 (2096) ◽  
pp. 20160292 ◽  
Author(s):  
M. McCullough ◽  
M. Small ◽  
H. H. C. Iu ◽  
T. Stemler
Keyword(s):  
Time Series ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Permutation Entropy ◽  
Multiscale Approach ◽  
Mapping Procedure ◽  
Interbeat Interval ◽  
Interval Time ◽  
Cardiac Dynamics ◽  
Interval Time Series

In this study, we propose a new information theoretic measure to quantify the complexity of biological systems based on time-series data. We demonstrate the potential of our method using two distinct applications to human cardiac dynamics. Firstly, we show that the method clearly discriminates between segments of electrocardiogram records characterized by normal sinus rhythm, ventricular tachycardia and ventricular fibrillation. Secondly, we investigate the multiscale complexity of cardiac dynamics with respect to age in healthy individuals using interbeat interval time series and compare our findings with a previous study which established a link between age and fractal-like long-range correlations. The method we use is an extension of the symbolic mapping procedure originally proposed for permutation entropy. We build a Markov chain of the dynamics based on order patterns in the time series which we call an ordinal network, and from this model compute an intuitive entropic measure of transitional complexity. A discussion of the model parameter space in terms of traditional time delay embedding provides a theoretical basis for our multiscale approach. As an ancillary discussion, we address the practical issue of node aliasing and how this effects ordinal network models of continuous systems from discrete time sampled data, such as interbeat interval time series. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

scholarly journals Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics

1996 ◽  
Vol 271 (4) ◽  
pp. R1078-R1084 ◽  
Author(s):  
N. Iyengar ◽  
C. K. Peng ◽  
R. Morin ◽  
A. L. Goldberger ◽  
L. A. Lipsitz
Keyword(s):  
Time Series ◽  
Long Range ◽  
Elderly Subjects ◽  
Fluctuation Analysis ◽  
Scaling Exponents ◽  
Interbeat Interval ◽  
Interval Time ◽  
Fractal Scaling ◽  
Age Related ◽  
Interval Time Series

We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.

Sign in / Sign up

Export Citation Format

Share Document