Multifractality in human heartbeat dynamics

Ivanov, Plamen Ch.; Amaral, Luı́s A. Nunes; Goldberger, Ary L.; Havlin, Shlomo; Rosenblum, Michael G.; Struzik, Zbigniew R.; Stanley, H. Eugene

doi:10.1038/20924

Cited by 1,422 publications

(910 citation statements)

References 28 publications

Supporting

Mentioning

849

Contrasting

Unclassified

Order By: Relevance

“…Our results show that the activity data contains important phase correlations which are canceled in the surrogate signal by the randomization of the Fourier phases, and that these correlations do not exist in the simulated scheduled activity. Further, our tests indicate that these nonlinear features are encoded in Fourier phase, suggesting an intrinsic nonlinear mechanism [27]. The similar value of α mag for all three protocols and all individuals, which is different from α mag = 0.5 obtained for the simulated scheduled activity and for the phase randomized data, confirms that the intrinsic dynamics possess nonlinear features that are independent of the daily and weekly schedules, reaction to the environment, the average level of activity, and the phase of the circadian pacemaker.…”

Section: Resultsmentioning

confidence: 67%

“…Since α mag ≈ 0.8( > 0.5), there are positive long-range correlations in the magnitude series of activity increments, indicating the existence of nonlinear properties related to Fourier phase interactions (Fig. 5b) [26,27]. To confirm that the observed positive correlations in the magnitude series indeed represent nonlinear features in the activity data, we do the following test: we generate a surrogate time series by performing a Fourier transform on the activity recording from the same subject during daily routine as in Fig.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Non-random fluctuations and multi-scale dynamics regulation of human activity

Ivanov

Chen

et al. 2004

Physica A: Statistical Mechanics and its Applications

Self Cite

137

157

View full text Add to dashboard Cite

We investigate if known extrinsic and intrinsic factors fully account for the complex features observed in recordings of human activity as measured from forearm motion in subjects undergoing their regular daily routine. We demonstrate that the apparently random forearm motion possesses dynamic patterns characterized by robust scale-invariant and nonlinear features. These patterns remain stable from one subject to another and are unaffected by changes in the average activity level that occur within individual subjects throughout the day and on different days of the week, since they persist during daily routine and when the same subjects undergo time-isolation laboratory experiments designed to account for the circadian phase and to control the known extrinsic factors. Further, by modeling the scheduled events imposed throughout the laboratory protocols, we demonstrate that they cannot account for the observed scaling patterns in activity fluctuations. We attribute these patterns to a previously unrecognized intrinsic nonlinear multi-scale control mechanism of human activity that is independent of known extrinsic factors such as random and scheduled events, as well as the known intrinsic factors which possess a single characteristic time scale such as circadian and ultradian rhythms.

show abstract

Section: Resultsmentioning

confidence: 67%

Section: Resultsmentioning

confidence: 99%

Non-random fluctuations and multi-scale dynamics regulation of human activity

Ivanov

Chen

et al. 2004

Physica A: Statistical Mechanics and its Applications

Self Cite

137

157

View full text Add to dashboard Cite

show abstract

“…Previous studies have used the range of Hölder exponent [21][22] or h(q min ) -h(q max ) [23] to quantify the multifractality degree of time series. However, estimates of the range of Hölder exponent often encounter a problem of numerical instability [24].…”

Section: P V (M )mentioning

confidence: 99%

Using multifractal detrended fluctuation analysis to assess sacral skin blood flow oscillations in people with spinal cord injury

Liao¹,

Jan²

2011

JRRD

View full text Add to dashboard Cite

Abstract-The purpose of this study was to investigate whether the multifractal detrended fluctuation analysis (MDFA) of skin blood flow oscillations (BFO) differed between nondisabled controls and people with spinal cord injury (SCI). The study of skin BFO has shown promise for assessing blood flow control mechanisms and risk for pressure ulcers. We recruited 23 subjects, including 11 people with SCI and 12 nondisabled controls. Thermally induced maximal sacral skin BFO were measured by laser Doppler flowmetry. MDFA was used to characterize nonlinear complexity of metabolic (0.0095 to 0.02 Hz), neurogenic (0.02 to 0.05 Hz), and myogenic (0.05 to 0.15 Hz) BFO. We found that maximal vasodilation was significantly smaller in people with SCI than in nondisabled controls (p < 0.05). MDFA showed that metabolic BFO exhibited less complexity in people with SCI (p < 0.05), neurogenic BFO exhibited less complexity in people with complete SCI (p < 0.05), and myogenic BFO did not show significant differences between people with SCI and nondisabled controls. This study demonstrated the feasibility of using the MDFA to characterize nonlinear complexity of BFO, which is related to vasodilatory functions in people with SCI.

show abstract

“…Accordingly, first applications of nonlinear dynamics tools in medical diagnostics have been met with success over the last decade [Focus Issue: Dynamical Disease: Mathematical Analysis of Human Illness, 1995; Kantz et al, 1998]. So far, however, research was limited either to only analyze data [Goldberger et al, 1988;Yamamoto & Hughson, 1994a;Cerutti et al, 1996;Poon & Merrill, 1997;Ivanov et al, 1999;Schmidt et al, 1999a;Yang et al, 2003;Wessel et al, 2004c], or develop models [Garfinkel et al, 1992;Wikswo et al, 1995;Gray et al, 1998;Witkowski et al, 1998;Ditto & Showalter, 1998 Heart, 2003]. A further aim of this tutorial, therefore, is, to go a qualitatively new step: the combination of data analysis and modeling.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Wessel

Malberg²,

Bauernschmitt

et al. 2007

Int. J. Bifurcation Chaos

100

106

View full text Add to dashboard Cite

In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring-, diagnosis-, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.Keywords: Cardiovascular physics; nonlinear data analysis and modeling; heart rate and blood pressure variability; baroreflex sensitivity. List of Abbreviations

show abstract

Multifractality in human heartbeat dynamics

Cited by 1,422 publications

References 28 publications

Non-random fluctuations and multi-scale dynamics regulation of human activity

Non-random fluctuations and multi-scale dynamics regulation of human activity

Using multifractal detrended fluctuation analysis to assess sacral skin blood flow oscillations in people with spinal cord injury

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Contact Info

Product

Resources

About