1/f2 and 1/f power spectra of short-term interbeat interval time series

1993 ◽  
Author(s):  
Ferenc Czegledy ◽  
Jose Katz
Keyword(s):  
Time Series ◽  
Power Spectra ◽  
Short Term ◽  
Title 1 ◽  
Interbeat Interval ◽  
Interval Time ◽  
Interval Time Series

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 Does the Temporal Asymmetry of Short-Term Heart Rate Variability Change during Regular Walking? A Pilot Study of Healthy Young Subjects

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xinpei Wang ◽  
Chang Yan ◽  
Bo Shi ◽  
Changchun Liu ◽  
Chandan Karmakar ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Short Term ◽  
Area Index ◽  
Temporal Asymmetry ◽  
Interval Time ◽  
Rr Interval ◽  
Rate Asymmetry ◽  
Interval Time Series

The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta’s index (PI), Guzik’s index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of <~0.03 Hz. Our pilot data showed a reduced PI but unchanged GI, SI, and AI during walking compared to resting seated position based on the raw data. Based on the wavelet-detrended data, reduced PI, SI, and AI were observed while GI still showed no significant changes. The reduced PI during walking based on both raw and detrended data which suggests less short-term HRA may underline the belief that vagal tone is withdrawn during low-intensity exercise. GI may not be sensitive to short-term HRA. The reduced SI and AI based on detrended data suggest that they may capture both short- and long-term HRA features and that the expected change in short-term HRA is amplified after removing the trend that is supposed to link to long-term component. Further studies with more subjects and longer measurements are warranted to validate our observations and to examine these additional hypotheses.

scholarly journals Entropy Analysis of Short-Term Heartbeat Interval Time Series during Regular Walking

Entropy ◽  
2017 ◽  
Vol 19 (10) ◽  
pp. 568 ◽  
Author(s):  
Bo Shi ◽  
Yudong Zhang ◽  
Chaochao Yuan ◽  
Shuihua Wang ◽  
Peng Li
Keyword(s):  
Time Series ◽  
Entropy Analysis ◽  
Short Term ◽  
Interval Time ◽  
Interval Time Series

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.

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