Short-term couplings of the cardiovascular system in pregnant women suffering from pre-eclampsia

149

127

Recently, methods have been developed to analyse couplings in dynamic systems. In the field of medical analysis of complex cardiovascular and cardiorespiratory systems, there is growing interest in how insights may be gained into the interaction between regulatory mechanisms in healthy and diseased persons. The couplings within and between these systems can be linear or nonlinear. However, the complex mechanisms involved in cardiovascular and cardiorespiratory regulation very likely interact with each other in a nonlinear way. Recent advances in nonlinear dynamics and information theory have allowed the multivariate study of information transfer between time series. They therefore might be able to provide additional diagnostic and prognostic information in medicine and might, in particular, be able to complement traditional linear coupling analysis techniques. In this review, we describe the approaches (Granger causality, nonlinear prediction, entropy, symbolization, phase synchronization) most commonly applied to detect direct and indirect couplings between time series, especially focusing on nonlinear approaches. We will discuss their capacity to quantify direct and indirect couplings and the direction (driver–response relationship) of the considered interaction between different biological time series. We also give their basic theoretical background, their basic requirements for application, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory coupling analyses.

Section: (I) Linear Methodsmentioning

confidence: 99%

Section: Applications Of Cardiovascular and Cardiorespiratory Couplinmentioning

confidence: 99%

Cardiovascular and cardiorespiratory coupling analyses: a review

Schulz

Adochiei

Edu

et al. 2013

149

127

“…This method could be also useful as an alternative to find the adequate structure of coupling and thus minimizing poor performances of the specific coupling structures to describe some casual relations. We are also aware of the major role of respiration [60] and related to the understanding of PE pathogenesis [54]. Therefore, adequate data processing, including respiration, should improve our results.…”

Section: Discussionmentioning

confidence: 99%

“…An estimation of the coupling structure of CV indicators has been performed using nonlinear additive autoregressive models with external input following the idea of Granger causality [54]. This coupling analysis shows that HRV, DBPV and SBPV respond to respiration; SBPV responds to DBPV and the latter to HRV.…”

Section: (C) Data Processing and Statisticsmentioning

confidence: 99%

Classification of cardiovascular time series based on different coupling structures using recurrence networks analysis

Ramírez-Ávila

Gapelyuk²,

Marwan

et al. 2013

Self Cite

We analyse cardiovascular time series with the aim of performing early prediction of preeclampsia (PE), a pregnancy-specific disorder causing maternal and foetal morbidity and mortality. The analysis is made using a novel approach, namely the ε -recurrence networks applied to a phase space constructed by means of the time series of the variabilities of the heart rate and the blood pressure (systolic and diastolic). All the possible coupling structures among these variables are considered for the analysis. Network measures such as average path length, mean coreness, global clustering coefficient and scale-local transitivity dimension are computed and constitute the parameters for the subsequent quadratic discriminant analysis. This allows us to predict PE with a sensitivity of 91.7 per cent and a specificity of 68.1 per cent, thus validating the use of this method for classifying healthy and preeclamptic patients.

“…Riedl et al (2010) address cardiac dynamics on a completely different scale and level of clinical application. Following more the dynamic network approach, they look at the coupling between respiration, systolic and diastolic blood pressure, and heart rate using nonlinear dynamical tools in an effort to both detect the dangerous pregnancy-related condition of pre-eclampsia and understand the systems involved.…”

Section: Experimental Nonlinear Dynamicsmentioning

confidence: 99%

Experimental nonlinear dynamics

Gluckman

2010

Although it is difficult to point to a definite origin of the field of nonlinear dynamics, it evolved in part from attempts to understand two fundamentally different types of deterministic systems-very simple systems with as few as three variables on the one side that generate erratic non-periodic dynamics, exemplified by the three-body problem; and nearly infinite dimensional systems, such as fluids, that transition from very well-behaved dynamics through a range of increasingly complex space and time-varying dynamics, exemplified by the transitions from laminar to turbulent hydrodynamic flows.By the 1980s, the nonlinear dynamics community fitted roughly into two camps, those looking at and harnessing low-dimensional chaos-the emergence of complex unpredictable behaviour from systems with few degrees of freedom; and those looking at pattern formation-or the emergence of order, or coherentstructured dynamics in space and time-from systems with infinite degrees of freedom. The classic experimental systems for the study of, and application of, chaos were circuits and pendulums, lasers and population dynamics. Pattern formation was studied in fluid flows, crystal growth dynamics, chemical reactions and biology. Often, there was cross-pollination between these camps in developed theoretical tools, computational hardware, data analysis and visualization technology and experimental approach. The objectives overall were to both understand the origins of the phenomena and provide insight into how to harness the origins of complexity, exemplified in the 1990s by the development of chaos control techniques.The current issue is a cross-sectional sampling of the broad experimental applications now addressed in the nonlinear dynamics community. It starts with a solution to a classic hydrodynamic problem in Metcalfe et al. (2010). The challenge addressed is how to optimally mix fluids-critical, for example, in chemical reactors and heat exchangers-with simple laminar, low-energy, flows. Efficient mixing brings arbitrary pairs of fluid particles together. The simplest of such flows does not have the mixture of convergent and divergent points needed to do so. But, as the authors demonstrate, super-positions of such flow patterns, achieved by alternating between boundary conditions, yield highly chaotic fluid trajectories and therefore ideal mixing.The next two articles address issues and applications in chaotic dynamics. In the 1990s, atomic physics was essentially re-born with the perfection of techniques to greatly cool and trap individual, or small numbers of, atoms (Letokhov et al. 1995). Such systems then allow for real experimental systems Networks abound both in nature and in our man-made world. Understanding the underlying structure of such networks, how the individual elements' dynamics determines and is modulated by the group dynamics, how information is processed and transmitted through such networks and how robust they are to changes in connections is currently a major undertaking. Kocarev et al. (2010) address one ...