Influence of paced maternal breathing on fetal–maternal heart rate coordination

Leeuwen, P. van; Geue, D.; Thiel, Marco; Cysarz, Dirk; Lange, Silke; Romano, M. Carmen; Wessel, Niels; Kurths, Jürgen; Grönemeyer, Dietrich

doi:10.1073/pnas.0901049106

Cited by 105 publications

(93 citation statements)

References 26 publications

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“…The study of these recurrences allows for a better understanding of the climate system; its past as well as its future [2][3][4]. The cardiorespiratory system is investigated by its recurrence properties to get insights in its mechanisms or to measure dysfunctions for diagnosing life threatening conditions [5][6][7][8][9]. Recurrence analysis is promising for investigating brain activity [10] and early detection of epileptic states [11].…”

Section: Introductionmentioning

confidence: 99%

Fast Computation of Recurrences in Long Time Series

Rawald

Sips

Marwan

et al. 2014

Springer Proceedings in Mathematics &Amp; Statistics

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We present an approach to recurrence quantification analysis (RQA) that allows to process very long time series fast. To do so, it utilizes the paradigm Divide and Recombine. We divide the underlying matrix of a recurrence plot (RP) into sub matrices. The processing of the sub matrices is distributed across multiple graphics processing unit (GPU) devices. GPU devices perform RQA computations very fast since they match the problem very well. The individual results of the sub matrices are recombined into a global RQA solution. To address the specific challenges of subdividing the recurrence matrix, we introduce means of synchronization as well as additional data structures. Outperforming existing implementations dramatically, our GPU implementation of RQA processes time series consisting of N ≈ 1, 000, 000 data points in about 5 minutes.

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Section: Introductionmentioning

confidence: 99%

Fast Computation of Recurrences in Long Time Series

Rawald

Sips

Marwan

et al. 2014

Springer Proceedings in Mathematics &Amp; Statistics

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“…It is important to recall that the above mentioned studies [3][4][5][6][7][8][9] have demonstrated only phase synchronization (PS) among such distinctly different complex systems, while the natural choice of GS in them has been largely neglected, except for the important study of Zheng etal [14] on low-dimensional systems without delay and without a substantial difference in their fractal dimension. Further, depending on the relation between PS and GS [26] in such systems, which remains unclear, our understanding on their evolutionary mechanism, dynamical and functional behavior may need to be reinvestigated.…”

mentioning

confidence: 99%

Global generalized synchronization in networks of different time-delay systems

Senthilkumar¹,

Suresh²,

Lakshmanan³

et al. 2013

EPL

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Abstract. -We show that global generalized synchronization (GS) exists in structurally different time-delay systems, even with different orders, with quite different fractal (Kaplan-Yorke) dimensions, which emerges via partial GS in symmetrically coupled regular networks. We find that there exists a smooth transformation in such systems, which maps them to a common GS manifold as corroborated by their maximal transverse Lyapunov exponent. In addition, an analytical stability condition using the Krasvoskii-Lyapunov theory is deduced. This phenomenon of GS in strongly distinct systems opens a new way for an effective control of pathological synchronous activity by means of extremely small perturbations to appropriate variables in the synchronization manifold.Synchronization is a ubiquitous nonlinear phenomenon serving as a platform for information processing in diverse natural and man made systems [1,2]. It has been investigated mainly in identical systems and in systems with parameter mismatch, with rare exceptions of distinctly (structurally) nonidentical systems [1,2]. However, in reality very often synchronization emerges in distinctly nonidentical systems such as respiratory arrhythmia between cardiovascular and respiratory systems [3], visual and motor systems [4], paced maternal breathing on fetal [5], different populations of species [6,7], in epidemics [8], in climatology [9], and many more. Considering the coherent coordination of living systems involving multiple organs such as brain, heart, lungs, limbs, etc., or machines consisting of distinct parts, cooperative evolution of distinct and often time-delayed systems is essential and challenging.Among various kinds of synchronization admitted by coupled nonlinear dynamical systems [1,2], the intricate phenomenon of generalized synchronization (GS) refers to (static) functional relationship between interacting systems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. While the phenomenon of GS has been well understood in unidirectionally coupled systems [10][11][12][13], it remain still in its infancy in bidirectionally coupled systems and in particular there exists only very limited results on GS, even in systems with parameter mismatches [14][15][16][17][18][19][20][21][22][23], and particularly in structurally different (nonidentical) systems with different fractal dimensions [24]. Thus, in general, the notion of GS in mutually coupled systems needs much indepth investigation and in particular in distinctly different systems with different fractal dimensions involving time-delay. Indeed, recent investigations have revealed that GS is essentially more likely to occur in complex networks (even with identical nodes) [15] due to the large heterogeneity (degree distribution) of many natural networks [21].It is important to recall that the above mentioned studies [3][4][5][6][7][8][9] have demonstrated only phase synchronization (PS) among such distinctly different complex systems, while the natural choice of GS in them has been largely negle...

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“…It was found that maternal relaxation, physical and mental activities could induce fetal autonomic response (9,10,11,12). In several studies maternal respiratory sinus arrhythmia (RSA) was determined as an evident factor of maternal and fetal heart rate synchronization (7,13).…”

Section: Introductionmentioning

confidence: 99%