Journal ArticleDOI
Dynamical assessment of physiological systems and states using recurrence plot strategies
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TLDR
This paper illustrates how recurrence plots can take single physiological measurements, project them into multidimensional space by embedding procedures, and identify time correlations (recurrences) that are not apparent in the one-dimensional time series.Abstract:
Physiological systems are best characterized as complex dynamical processes that are continuously subjected to and updated by nonlinear feedforward and feedback inputs. System outputs usually exhibit wide varieties of behaviors due to dynamical interactions between system components, external noise perturbations, and physiological state changes. Complicated interactions occur at a variety of hierarchial levels and involve a number of interacting variables, many of which are unavailable for experimental measurement. In this paper we illustrate how recurrence plots can take single physiological measurements, project them into multidimensional space by embedding procedures, and identify time correlations (recurrences) that are not apparent in the one-dimensional time series. We extend the original description of recurrence plots by computing an array of specific recurrence variables that quantify the deterministic structure and complexity of the plot. We then demonstrate how physiological states can be assessed by making repeated recurrence plot calculations within a window sliding down any physiological dynamic. Unlike other predominant time series techniques, recurrence plot analyses are not limited by data stationarity and size constraints. Pertinent physiological examples from respiratory and skeletal motor systems illustrate the utility of recurrence plots in the diagnosis of nonlinear systems. The methodology is fully applicable to any rhythmical system, whether it be mechanical, electrical, neural, hormonal, chemical, or even spacial.read more
Citations
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Journal ArticleDOI
Recurrence plots for the analysis of complex systems
TL;DR: The aim of this work is to provide the readers with the know how for the application of recurrence plot based methods in their own field of research, and detail the analysis of data and indicate possible difficulties and pitfalls.
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Kubios HRV - Heart rate variability analysis software
Mika P. Tarvainen,Juha-Pekka Niskanen,Jukka A. Lipponen,Perttu O. Ranta-aho,Pasi A. Karjalainen +4 more
TL;DR: Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis that includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection.
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Recurrence-plot-based measures of complexity and their application to heart-rate-variability data.
TL;DR: Applying measures of complexity based on vertical structures in recurrence plots and applying them to the logistic map as well as to heart-rate-variability data is able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event.
Journal ArticleDOI
Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals -- a review.
Eberhard von Borell,Jan Langbein,Gérard Després,Sven Hansen,Christine Leterrier,Jeremy N. Marchant-Forde,Ruth M. Marchant-Forde,Michela Minero,Elmar Mohr,Armelle Prunier,D. Valance,Isabelle Veissier +11 more
TL;DR: Data from earlier research demonstrate that HRV is a promising approach for evaluating stress and emotional states in animals, and has the potential to contribute much to the understanding and assessment of the underlying neurophysiological processes of stress responses and different welfare states in farm animals.
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Automated EEG analysis of epilepsy: A review
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References
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Use of recurrence plots in analysis of time-series data
TL;DR: Geometrical properties of the authors' plots provide dimensional complexity and local rate of divergence estimates and Eckmann, Kamphorst and Ruelle's idea of a recurrence plot, which they say helps appreciate the dynamics of a time series without either of these constraints.