Renormalised entropy: a new method of non-linear dynamics for the analysis of heart rate variability

Wessel, Niels; Voss, Andreas; Kurths, Jüergen; Saparin, Peter; Witt, Annette; Kleiner, H.J.; Dietz, Rainer

doi:10.1109/cic.1994.470230

Cited by 15 publications

(13 citation statements)

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“…Applications of renormalised entropy to heart rate data based on the fast fourier transform (FFT) have been introduced previously (Voss et al, 1993;WESSEL et al, 1994;KURTHS et al, 1995). However, this method suffers from a potential lack of reproducibility and time instability.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of renormalised entropy for risk stratification using heart rate variability data

Wessel

Voss

Kurths

et al. 2000

Med. Biol. Eng. Comput.

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Standard time and frequency parameters of heart rate variability (HRV) describe only linear and periodic behaviour, whereas more complex relationships cannot be recognised. A method that may be capable of assessing more complex properties is the non-linear measure of 'renormalised entropy.' A new concept of the method, RE(AR), has been developed, based on a non-linear renormalisation of autoregressive spectral distributions. To test the hypothesis that renormalised entropy may improve the result of high-risk stratification after myocardial infarction, it is applied to a clinical pilot study (41 subjects) and to prospective data of the St George's Hospital post-infarction database (572 patients). The study shows that the new RE(AR) method is more reproducible and more stable in time than a previously introduced method (p<0.001). Moreover, the results of the study confirm the hypothesis that on average, the survivors have negative values of RE(AR) (-0.11+/-0.18), whereas the non-survivors have positive values (0.03+/-0.22, p<0.01). Further, the study shows that the combination of an HRV triangular index and RE(AR) leads to a better prediction of sudden arrhythmic death than standard measurements of HRV. In summary, the new RE(AR) method is an independent measure in HRV analysis that may be suitable for risk stratification in patients after myocardial infarction.

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Section: Introductionmentioning

confidence: 99%

“…The constant 3.5 was chosen empirically (WESSEL et al, 1994). The three filtering methods differ in their ways of replacing t values that are not accepted, the original value t is method 1 (F1): replaced by the respective value 7 of the filtered time series method 2 (F2): replaced by linear interpolation method 3 (F3): removed.…”

mentioning

confidence: 99%

Evaluation of renormalised entropy for risk stratification using heart rate variability data

Wessel

Voss

Kurths

et al. 2000

Med. Biol. Eng. Comput.

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“…Saparin proposed a procedure for calculating this quantity from time series and applied it to the logistic map. Applications of renormalized entropy to physiological data were previously introduced [Kurths et al, 1995;Voss et al, 1996b;Wessel et al, 1994;Kopitzki et al, 1998]. That method, however, suffers from a potential lack of reproducibility for heart rate data.…”

Section: Renormalized Entropymentioning

confidence: 99%

“…the length of the selected tachogram parts varied from 3 to 9. We choose this range to cover an interval up to 9, which is a typical order of an autoregressive model for short-term HRV tachograms [Wessel et al, 1994]. The evolution time and the delay are defined as T = 1, 2, 3 and τ = 1, 2, 3 respectively.…”

Section: Finite-time Growth Ratesmentioning

confidence: 99%

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Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Wessel

Malberg²,

Bauernschmitt

et al. 2007

Int. J. Bifurcation Chaos

102

107

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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

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Duale Sequenzmethode zur Analyse der spontanen Baroreflexsensitivität bei Patienten mit dilatativer Kardiomyopathie

et al. 1999

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Dual sequence method for analysis of the spontaneous baroreceptor sensitivity in patients with dilated cardiomyopathySummary The analysis of heart rate variability (HRV) and blood pressure variability (BPV) improves the characterization of patients with dilated cardiomyopathy (DCM). In this study we tested the hypothesis that patients with DCM and controls show a different behavior in the baroreflex (BR) regulation.In contrast to other methods, the new dual sequence method (DSM) analyzes the baroreflex sensitivity (BRS) as a response of the heart rate (interbeat interval, IBI) on dual spontaneous fluctuations of blood pressure (BP). The DSM includes the analysis of bradycardiac fluctuations (an increase of BP causes an increase of IBI) and tachycardiac fluctuations (decrease of BP causes a decrease of IBI) to obtain enhanced information about the sympathetic-vagal regulation.DCM patients show a 40-50 % lower number of correlated blood pressure-heart rate fluctuations (DCM patients: male 154 ± 93, female 93 ± 40 vs. control group: m 245 ± 112, f 150 ± 55, p < 0.05). The BRS in DCM patients is significantly lower than in controls (5.2 ± 1.9 vs. 8.0 ± 5.4 (ms/mm Hg), p < 0.05). Using the DSM the discriminant function analysis (6 parameters) classifies correctly 84 % of DCM patients and the control group. Using the classical sequence method, only 76 % were correctly classified.The DSM is a useful method for analyzing the BRS based on the spontaneous BR to obtain an increased classification of patients with DCM. BRS in patients with DCM is significantly reduced and apparently more ineffective.

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Renormalised entropy: a new method of non-linear dynamics for the analysis of heart rate variability

Cited by 15 publications

References 4 publications

Evaluation of renormalised entropy for risk stratification using heart rate variability data

Evaluation of renormalised entropy for risk stratification using heart rate variability data

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Duale Sequenzmethode zur Analyse der spontanen Baroreflexsensitivität bei Patienten mit dilatativer Kardiomyopathie

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