Detrended time series (DTS) analysis reveals low heart rate variability (HRV) of intra-uterine growth restricted (IUGR) fetuses–A magnetocardiographic study

2006 ◽  
Vol 195 (6) ◽  
pp. S230
Author(s):  
Rathinaswamy Govindan ◽  
Eric Siegel ◽  
James Wilson ◽  
Hari Eswaran ◽  
Hubert Preissl ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Uterine Growth

On the fractal nature of heart rate variability in humans: effects of respiratory sinus arrhythmia

1995 ◽  
Vol 269 (2) ◽  
pp. H480-H486 ◽  
Author(s):  
Y. Yamamoto ◽  
J. O. Fortrat ◽  
R. L. Hughson
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Graining ◽  
Resting Heart Rate ◽  
Breathing Frequency ◽  
Fractal Nature ◽  
Sinus Arrhythmia ◽  
Spectral Exponent ◽  
Random Fractal

The purpose of the present study was to investigate the basic fractal nature of the variability in resting heart rate (HRV), relative to that in breathing frequency (BFV) and tidal volume (TVV), and to test the hypothesis that fractal HRV is due to the fractal BFV and/or TVV in humans. In addition, the possible fractal nature of respiratory volume curves (RVC) and HRV was observed. In the first study, eight subjects were tested while they sat quietly in a comfortable chair for 60 min. Beat-to-beat R-R intervals, i.e., HRV, and breath-by-breath BFV and TVV were measured. In the second study, six subjects were tested while they were in the supine position for 20-30 min. The RVC was monitored continuously together with HRV. Coarse-graining spectral analysis (Yamamoto, Y., and R. L. Hughson, Physica D 68: 250-264, 1993) was applied to these signals to evaluate the percentage of random fractal components in the time series (%Fractal) and the spectral exponent (beta), which characterizes irregularity of the signals. The estimates of beta were determined for each variable only over the range normally used to evaluate HRV. Values for %Fractal and beta of both BFV and TVV were significantly (P < 0.05) greater than those for HRV. In addition, there was no significant (P > 0.05) correlation between the beta values of HRV relative to either BFV (r = 0.14) or TVV (r = 0.34). RVC showed a smooth oscillation as compared with HRV; %Fractal for RVC (42.3 +/- 21.7%, mean +/- SD) was significantly (P < 0.05) lower than that for HRV (78.5 +/- 4.2%).(ABSTRACT TRUNCATED AT 250 WORDS)

An application of fractional differintegration to heart rate variability time series

2013 ◽  
Vol 111 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miguel A. García-González ◽  
Mireya Fernández-Chimeno ◽  
Lluis Capdevila ◽  
Eva Parrado ◽  
Juan Ramos-Castro
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability

Heart Rate Variability in Intensive Care

1998 ◽  
Vol 13 (5) ◽  
pp. 252-265 ◽  
Author(s):  
Brahm Goldstein ◽  
Timothy G. Buchman
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Time Series Analysis ◽  
Fetal Distress ◽  
Heart Beat ◽  
Dynamic Interactions ◽  
Series Analysis ◽  
Injured Patients

Clinicians have long been aware that the normal oscillations in a heart beat are lost during fetal distress, during the early stages of heart failure, with advanced aging, and with critical illness and injury. However, these oscillations, or variability in heart rate and other cardiovascular signals, have largely been ignored or discounted as variances from the mean or average values. It is becoming increasingly clear that these oscillations reflect the dynamic interactions of many physiologic processes, including neuroautonomic regulation of heart rate and blood pressure. We present a synthesis and review of the current literature concerning heart rate variability with special reference to intensive care. This article describes the background of time series analysis of heart rate variability including time and frequency domain and nonlinear measurements. The implications and potential for time series analysis of variability in cardiovascular signals in clinical diagnosis and management of critically ill and injured patients are discussed.

New indices for quantification of the power spectrum of heart rate variability time series without the need of any frequency band definition

2011 ◽  
Vol 32 (8) ◽  
pp. 995-1009 ◽  
Author(s):  
M A García-González ◽  
M Fernández-Chimeno ◽  
J Ferrer ◽  
R M Escorihuela ◽  
E Parrado ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Power Spectrum ◽  
Frequency Band

Use of a prototype pulse oximeter for time series analysis of heart rate variability

2015 ◽  
Author(s):  
Erika González ◽  
Jehú López ◽  
Mathieu Hautefeuille ◽  
Víctor Velázquez ◽  
Jésica Del Moral
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Time Series Analysis ◽  
Pulse Oximeter ◽  
Series Analysis

scholarly journals Exploring the Relationship between Geomagnetic Activity and Human Heart Rate Variability

2019 ◽  
Author(s):  
Matthew Mattoni ◽  
Sangtae Ahn ◽  
Carla Fröhlich ◽  
Flavio Fröhlich
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Solar Activity ◽  
Geomagnetic Activity ◽  
Low Frequency ◽  
Physiological Measurement ◽  
Small Effect Size ◽  
Analysis Strategy ◽  
Geophysical Time Series

AbstractBoth geomagnetic and solar activity fluctuate over time and have been proposed to affect human physiology. One physiological measurement that has been previously investigated in this context, heart rate variability (HRV), has substantial health implications regarding the ability to adapt to stressors and has been shown to be altered in many cardiovascular and neurological disorders. Intriguingly, previous work found significant, strong correlations between HRV and geomagnetic/solar activity. In an attempt to replicate these findings, we simultaneously measured HRV from 20 healthy participants during a thirty-day period. In agreement with previous work, we found several significant correlations between HRV and geophysical time-series. However, after correction for autocorrelation, which is inherent in time-series, the only significant results were an increase in very low frequency during higher local geomagnetic activity and a geomagnetic anticipatory decrease in heart rate a day before higher global geomagnetic activity. Both correlations were very low. The loss of most significant effects after this correction suggests that previous findings may be a result of autocorrelation. A further note of caution is required since our and the previous studies in the field do not correct for multiple comparisons given the exploratory analysis strategy. We thus conclude that the effects of geomagnetic and solar activity are (if present) most likely of very small effect size and question the validity of the previous studies given the methodological concerns we have uncovered in our work.

scholarly journals Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

2020 ◽  
Vol 11 ◽  
Author(s):  
Shahul Mujib Kamal ◽  
Mohammad Hossein Babini ◽  
Ondrej Krejcar ◽  
Hamidreza Namazi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Fractal Dimension ◽  
Daily Life ◽  
Fractal Theory ◽  
Sample Entropy ◽  
Physiological Signals ◽  
Everyday Activity ◽  
First Time

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.

One-dimensional, nonlinear determinism characterizes heart rate pattern during paced respiration

1998 ◽  
Vol 275 (3) ◽  
pp. H1092-H1102 ◽  
Author(s):  
Katrin Suder ◽  
Friedhelm R. Drepper ◽  
Michael Schiek ◽  
Hans-Henning Abel
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Respiratory Sinus Arrhythmia ◽  
Sinus Arrhythmia ◽  
Dynamic Pattern ◽  
One Dimensional ◽  
Circle Maps ◽  
Paced Respiration ◽  
Heart Rate Pattern

This study focuses on the dynamic pattern of heart rate variability in the frequency range of respiration, the so-called respiratory sinus arrhythmia. Forty experimental time series of heart rate data from four healthy adult volunteers undergoing a paced respiration protocol were used as an empirical basis. For pacing-cycle lengths >8 s, the heartbeat intervals are shown to obey a rule that can be expressed by a one-dimensional circle map (next-angle map). Circle maps are introduced as a new type of model for time series analyses to characterize the nonlinear dynamic pattern underlying the respiratory sinus arrhythmia during voluntary paced respiration. Although these maps are not chaotic, the dynamic pattern shows typical imprints of nonlinearity. By starting from a piecewise linear model, which describes the different circle maps obtained from the empirical time series for various pacing frequencies, time invariant measures can be introduced that characterize the dynamic pattern of heart rate variability during voluntary slow-paced respiration.

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