Quantifying heart rate dynamics using different approaches of symbolic dynamics

Cysarz, Dirk; Porta, Alberto; Montano, Nicola; Leeuwen, P. van; Kurths, Jürgen; Wessel, Niels

doi:10.1140/epjst/e2013-01854-7

Cited by 71 publications

(74 citation statements)

References 23 publications

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“…Previous findings with respect to the transformation of RR interval data obtained from a head-up tilt table challenge with healthy adult subjects showed slightly different results with respect to the performance of the transformations [4]. The categories 0V% and 1V% of the Binary -transformation using the threshold τ showed the largest number of statistically significant individual slopes in the diagram 0V% (or 1V%) versus tilt table inclination.…”

Section: Discussionmentioning

confidence: 73%

Strategies of symbolization in cardiovascular time series to test individual gestational development in the fetus

Cysarz

Edelhäuser

Leeuwen

2015

Phil. Trans. R. Soc. A.

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The analysis of symbolic dynamics applied to physiological time series retrieves dynamical properties of the underlying regulation which are robust against the symbolic transformation. In this study, three different transformations to produce a symbolic series were applied to fetal RR interval series to test whether they reflect individual changes of fetal heart rate variability in the course of pregnancy. Each transformation was applied to 215 heartbeat datasets obtained from 11 fetuses during the second and the third trimester of pregnancy (at least 10 datasets per fetus, median 17). In the symbolic series, the occurrence of symbolic sequences of length 3 was categorized according to the amount of variations in the sequence: no variation of the symbols, one variation, two variations. Linear regression with respect to gestational age showed that the individual course during pregnancy performed best using a binary transformation reflecting whether the RR interval differences are below or above a threshold. The median goodness of fit of the individual regression lines was 0.73 and also the variability among the individual slopes was low. Other transformations to symbolic dynamics performed worse but were still able to reflect the individual progress of fetal cardiovascular regulation.

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Section: Discussionmentioning

confidence: 73%

Strategies of symbolization in cardiovascular time series to test individual gestational development in the fetus

Cysarz

Edelhäuser

Leeuwen

2015

Phil. Trans. R. Soc. A.

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“…Cysarz et al [3,70] investigated different approaches for the transformation of the original time series to the symbolic time series. They investigated three different transformation methods as: (1) symbolization according to the deviation from the average time series ( -method), (2) symbolization according to several equidistant levels between the minimum and maximum of the time series (max-min-method), (3) binary symbolization of the first derivative of the time series (binary -codingmethod).…”

Section: Symbolic Dynamicsmentioning

confidence: 99%

“…In cardiovascular physiology, symbolic dynamics was applied, e.g. for the determination of the sympathovagal balance towards a sympathetic activation and vagal withdrawal during graded orthostatic challenge [2], for monitoring the complexity of the cardiac control [3], to evaluate the maternal baroreflex regulation during gestation [4], to investigate autonomic regulation (cardiorespiratory system) during acute psychosis in patients suffering from paranoid schizophrenia and their healthy first-degree relatives [5], to detect pathological states and improve the risk stratification in cardiology [6]. In the field of neuroscience, symbolic dynamics was applied, e.g.…”

Section: Introductionmentioning

confidence: 99%

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

Signorini

Magenes

Ferrario

et al. 2017

Complexity and Nonlinearity in Cardiovascular Signals

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Methods from nonlinear dynamics have shown new insights into alterations of the cardiovascular system under various physiological and pathological conditions, and thus providing additional prognostic information. In this chapter prominent complexity methods of non-linear dynamics as symbolic dynamics, Poincaré plot analyses, and compression entropy are introduced and their algorithmic implementations and application examples in clinical trials are provided. Especially, we will give their basic theoretical background, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory time series analyses. IntroductionLinear time and frequency domain measures are often not sufficient enough to quantify the complex dynamics of physiological systems and their related time series. Therefore, various efforts have been made to apply nonlinear complexity measures to analyze, e.g. the heart rate variability (HRV) [1]. These approaches differ from the traditional time-and frequency domain HRV analyses because they quantify the signal properties instead of assessing only the magnitude or the frequency power of the heart rate time series. They assess the self-affinity of heartbeat fluctuations over multiple time scales (fractal measures); the regularity/irregularity or randomness of heartbeat fluctuations (entropy measures); the coarse-grained dynamics of HR fluctuations based on symbols (symbolic dynamics) and the heartbeat dynamics based on a simplified phase-space embedding [1].Symbolic dynamics is based on a coarse graining of the dynamics of a signal. The time series (in our cases the ECG or the noninvasively recorded blood pressure curve) are transformed into symbol sequences with symbols of a given alphabet. Some detail information is lost but the coarse dynamic behavior retains and

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“…Each region is associated with a specific symbolic value and uniquely mapped to a particular symbol depending on the region. The number of symbols and symbolization strategy are selected based on how much of the original information is retained in the sequence of symbols, and the sensitivity of the results to the choice of partition requires careful evaluation [54][55][56]. In broad applications, symbolization of experimental data provides better efficiency when finding and quantifying information from a system, reducing sensitivity to measurement noise and increasing the efficiency of numerical computations [57].…”

Section: Symbolization Of Electroencephalographymentioning

confidence: 99%

Assessing levels of consciousness with symbolic analysis

Lee

Blain-Moraes

Mashour

2015

Phil. Trans. R. Soc. A.

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‘Covert consciousness’ is a state in which consciousness is present without the capacity for behavioural response, and it can occur in patients with intraoperative awareness or unresponsive wakefulness syndrome. To detect and prevent this undesirable state, it is critical to develop a reliable neurobiological assessment of an individual's level of consciousness that is independent of behaviour. One such approach that shows potential is measuring surrogates of cortical communication in the brain using electroencephalography (EEG). EEG is practicable in clinical application, but involves many fundamental signal processing problems, including signal-to-noise ratio and high dimensional complexity. Symbolic analysis of EEG can mitigate these problems, improving the measurement of brain connectivity and the ability to successfully assess levels of consciousness. In this article, we review the problem of covert consciousness, basic neurobiological principles of consciousness, current methods of measuring brain connectivity and the advantages of symbolic processing, with a focus on symbolic transfer entropy (STE). Finally, we discuss recent advances and clinical applications of STE and other symbolic analyses to assess levels of consciousness.

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Quantifying heart rate dynamics using different approaches of symbolic dynamics

Cited by 71 publications

References 23 publications

Strategies of symbolization in cardiovascular time series to test individual gestational development in the fetus

Strategies of symbolization in cardiovascular time series to test individual gestational development in the fetus

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

Assessing levels of consciousness with symbolic analysis

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