Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability

Porta, Alberto; Tobaldini, Eleonora; Guzzetti, Stefano; Furlan, Raffaello; Montano, Nicola; Gnecchi‐Ruscone, Tomaso

doi:10.1152/ajpheart.00006.2007

Cited by 262 publications

(337 citation statements)

References 20 publications

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“…In turn, this increased predictive information during T is mirrored by the significantly higher information storage not compensated by variations of the information transfer T S,R→H ; the latter decreased significantly during T when computed through variance measures (Figure 6d), while it was unchanged when computed through entropy measures (Figure 6a). These results confirm those of a large number of previous studies reporting a reduction of the dynamical complexity (or equivalently an increase of the regularity) of HP variability in the orthostatic position, reflecting the well-known shift of the sympatho-vagal balance towards sympathetic activation and parasympathetic deactivation [40,51,52].…”

Section: Information Decomposition Of Heart Period Variability Duringsupporting

confidence: 91%

Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

Faes

Porta

Nollo

et al. 2016

Entropy

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108

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Abstract:The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amounts eliciting the specific contribution of assigned source systems to the target dynamics, and amounts reflecting information modification through the balance between redundant and synergetic interaction between systems. These decompositions are formulated quantifying information either as the variance or as the entropy of the investigated processes, and their exact computation for the case of linear Gaussian processes is presented. The theoretical properties of the resulting measures are first investigated in simulations of vector autoregressive processes. Then, the measures are applied to assess information dynamics in cardiovascular networks from the variability series of heart period, systolic arterial pressure and respiratory activity measured in healthy subjects during supine rest, orthostatic stress, and mental stress. Our results document the importance of combining the assessment of information storage, transfer and modification to investigate common and complementary aspects of network dynamics; suggest the higher specificity to alterations in the network properties of the measures derived from the decompositions; and indicate that measures of information transfer and information modification are better assessed, respectively, through entropy-based and variance-based implementations of the framework.

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Section: Information Decomposition Of Heart Period Variability Duringsupporting

confidence: 91%

Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

Faes

Porta

Nollo

et al. 2016

Entropy

Self Cite

108

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“…The decreased percentage of the HP patterns more variable than the 0V ones (i.e. 1V, 2LV and 2UV) during PRESY also suggested an important vagal withdrawal [21]. This observation was corroborated by the dramatic drop of the HP variance reported in table 1.…”

Section: Discussionsupporting

confidence: 54%

“…Given that in the HP variability an increased presence of 0V patterns and a decreased incidence of more variable ones, such that observed during graded head-up tilt [21], is associated with a loss of complexity of the cardiac control [35], we suggest that the evoked syncope is characterized by a dramatic reduction of complexity of the cardiac control. This relevant reduction of complexity might be again a consequence of the exaggerated sympathetic activation limiting considerably the possibility to respond to stimulatory inputs and the range of temporal scales involved in cardiovascular regulation [35].…”

Section: Discussionmentioning

confidence: 73%

Conditional symbolic analysis detects nonlinear influences of respiration on cardiovascular control in humans

Porta

Marchi²,

Bari

et al. 2015

Phil. Trans. R. Soc. A.

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We propose a symbolic analysis framework for the quantitative characterization of complex dynamical systems. It allows the description of the time course of a single variable, the assessment of joint interactions and an analysis triggered by a conditioning input. The framework was applied to spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and integrated muscle sympathetic nerve activity (MSNA) with the aim of characterizing cardiovascular control and nonlinear influences of respiration at rest in supine position, during orthostatic challenge induced by 80° head-up tilt (TILT) and about 3 min before evoked pre-syncope signs (PRESY). The approach detected (i) the exaggerated sympathetic modulation and vagal withdrawal from HP variability and the increased presence of fast MSNA variability components during PRESY compared with TILT; (ii) the increase of the SAP–HP coordination occurring at slow temporal scales and a decrease of that occurring at faster time scales during PRESY compared with TILT; (iii) the reduction of the coordination between fast MSNA and SAP patterns during TILT and PRESY; (iv) the nonlinear influences of respiration leading to an increased likelihood to observe the abovementioned findings during expiration compared with inspiration one. The framework provided simple, quantitative indexes able to distinguish experimental conditions characterized by different states of the autonomic nervous system and to detect the early signs of a life threatening situation such as postural syncope.

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“…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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Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability

Cited by 262 publications

References 20 publications

Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

Conditional symbolic analysis detects nonlinear influences of respiration on cardiovascular control in humans

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

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