High resolution ECG, heart rate variability and nonlinear dynamics: tools for high risk stratification

Voss, Andreas; Dietz, Rainer; Fiehring, H; Kleiner, H.J.; Kurths, Jürgen; Saparin, Peter; Vossing, H.J.; Witt, Annette

doi:10.1109/cic.1993.378454

Cited by 22 publications

(18 citation statements)

References 6 publications

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“…SDyn was introduced by Hadamard (1898) and allows a simple description of a system's dynamics with a limited amount of symbols. SDyn are suitable to describe the global short-time dynamics of beat-to-beat variability (Voss et al 1993(Voss et al , 1996(Voss et al , 1998Kurths et al 1995). At first, time series were transformed into a symbol sequence of four symbols with the alphabet AZ{0, 1, 2, 3} to classify the dynamic changes within that time series.…”

Section: (Iii) Compression Entropymentioning

confidence: 99%

Methods derived from nonlinear dynamics for analysing heart rate variability

Voss

Schulz

Schroeder

et al. 2008

Phil. Trans. R. Soc. A.

519

417

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Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time-and frequencydomain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono-and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

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Section: (Iii) Compression Entropymentioning

confidence: 99%

Methods derived from nonlinear dynamics for analysing heart rate variability

Voss

Schulz

Schroeder

et al. 2008

Phil. Trans. R. Soc. A.

519

417

View full text Add to dashboard Cite

show abstract

“…Furthermore, three further measures of non-linear dynamics were calculated from symbolic dynamics (20,21). Symbolic dynamics is based on the transformation of the time series into symbols.…”

Section: Variability Analysis Of Hr and Bpmentioning

confidence: 99%

Altered heart rate and blood pressure variability in mice lacking the Mas protooncogene

Walther

Wessel²,

Kang

et al. 2000

Braz J Med Biol Res

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Heart rate variability is a relevant predictor of cardiovascular risk in humans. A significant genetic influence on heart rate variability is suggested, although the genes involved are ill-defined. The Masprotooncogene encodes a G-protein-coupled receptor with seven transmembrane domains highly expressed in testis and brain. Since this receptor is supposed to interact with the signaling of angiotensin II, which is an important regulator of cardiovascular homeostasis, heart rate and blood pressure were analyzed in Mas-deficient mice. Using a femoral catheter the blood pressure of mice was measured for a period of 30 min and 250 data values per second were recorded. The mean values and range of heart rate and blood pressure were then calculated. Neither heart rate nor blood pressure were significantly different between knockout mice and controls. However, high resolution recording of these parameters and analysis of the data by non-linear dynamics revealed significant alterations in cardiovascular variability in Mas-deficient animals. In particular, females showed a strong reduction of heart rate variability. Furthermore, the data showed an increased sympathetic tone in knockout animals of both genders. The marked alterations detected in Mas-deficient mice of both genders suggest that the Mas-protooncogene is an important determinant of heart rate and blood pressure variability.

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“…Thus it is not realistic to restrict HRV analysis only to linear methods. The application of non-linear methods in addition to the traditional ones seems to be promising in this respect (GOLDBERGER et al, 1988;Voss et al, 1993;KURTHS et al, 1995;SCHREIBER, 1997;MAKIKALLIO et al, 1997;SCHAFER et al, 1998;WESSEL et al, 2000). Nevertheless, many non-linear methods require rather long, stationary time series and are not easily applicable to the data of cardiac periods.…”

Section: Introductionmentioning

confidence: 96%

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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High resolution ECG, heart rate variability and nonlinear dynamics: tools for high risk stratification

Cited by 22 publications

References 6 publications

Methods derived from nonlinear dynamics for analysing heart rate variability

Methods derived from nonlinear dynamics for analysing heart rate variability

Altered heart rate and blood pressure variability in mice lacking the Mas protooncogene

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

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