Dynamical assessment of physiological systems and states using recurrence plot strategies
01 Feb 1994-Journal of Applied Physiology (American Physiological Society)-Vol. 76, Iss: 2, pp 965-973
TL;DR: 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.
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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.
2,993 citations
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.
1,841 citations
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.
Abstract: The knowledge of transitions between regular, laminar or chaotic behaviors is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods that, however, require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart-rate-variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e., chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our measures to the heart-rate-variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.
890 citations
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.
766 citations
Cites background or methods from "Dynamical assessment of physiologic..."
...Recurrence quantification analysis (RQA) may be applied to IBI data to detect hidden rhythms and non-linear deterministic structures of HRV in higher dimensional space [165,166]....
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...For a more detailed description of the mathematical background of RQA, several detailed methodological papers have been published previously [163,165,166,171]....
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...Since then, six parameters have been introduced that may be used to quantitatively assess recurrence plots(1) [165,166]....
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TL;DR: This review discusses various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail, and briefly presents the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting.
Abstract: Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify subtle but critical changes in the EEG waveform by visual inspection, thus opening up a vast research area for biomedical engineers to develop and implement several intelligent algorithms for the identification of such subtle changes. Moreover, the EEG signals are nonlinear and non-stationary in nature, which contribute to further complexities related to their manual interpretation and detection of normal and abnormal (interictal and ictal) activities. Hence, it is necessary to develop a Computer Aided Diagnostic (CAD) system to automatically identify the normal and abnormal activities using minimum number of highly discriminating features in classifiers. It has been found that nonlinear features are able to capture the complex physiological phenomena such as abrupt transitions and chaotic behavior in the EEG signals. In this review, we discuss various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail. We also briefly present the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting.
601 citations
References
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TL;DR: In this paper, the correlation exponent v is introduced as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise, and algorithms for extracting v from the time series of a single variable are proposed.
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TL;DR: In this article, a graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples, and the tool can be used to measure the time complexity of a dynamical system.
Abstract: A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples.
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"Dynamical assessment of physiologic..." refers background in this paper
...(7) have shown that the length of these short upward diagonal lines is inversely proportional to the largest positive Lyapunov exponent, one hallmark for chaos in stationary systems....
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...(7) discuss the utility of recurrence plots in revealing subtle time correlations (multidimensional perspective) in otherwise benign time series (l-dimensional perspective)....
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...Because recurrence plots were originally designed to detect hidden rhythms embedded within complex waveforms independent of stationarity restrictions (7), we postulated that our recurrence plot variables would be more sensitive than standard technologies in detecting physiological state changes....
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...(7), was originally designed to locate recurring patterns (hidden rhythms) and nonstationarities (drifts) in experimental data sets....
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TL;DR: Recurrence plots have been advocated as a useful diagnostic tool for the assessment of dynamical time series by quantifying certain features of these plots which may be helpful in determining embeddings and delays.
1,020 citations
TL;DR: In this paper, it was shown that the correlation dimension of the Grassberger-Procaccia algorithm cannot exceed the value 2 log 10N if N is the number of points in the time series, and when this bound is saturated it is thus not legitimate to conclude that low dimensional dynamics is present.
631 citations
"Dynamical assessment of physiologic..." refers background in this paper
...Alternately, increasing the size of data strings introduces nonstationarity trending that smears out characteristic peaks in the frequency spectrum (Fourier analysis), yields false estimations of the number of operating variables (correlation dimension calculations), and mistakenly identifies the presence of dynamical chaos (positive Lyapunov exponents) (8)....
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