scholarly journals Cardiovascular and cardiorespiratory coupling analyses: a review

2013 ◽  
Vol 371 (1997) ◽  
pp. 20120191 ◽  
Author(s):  
Steffen Schulz ◽  
Felix-Constantin Adochiei ◽  
Ioana-Raluca Edu ◽  
Rico Schroeder ◽  
Hariton Costin ◽  
...  
Keyword(s):  
Time Series ◽  
Information Transfer ◽  
Phase Synchronization ◽  
Theoretical Background ◽  
Nonlinear Prediction ◽  
Prognostic Information ◽  
Driver Response ◽  
Biological Time ◽  
Cardiorespiratory Coupling ◽  
Medical Analysis

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.

scholarly journals Phase Synchronization Is the Amplified Result by the Hilbert Transform

2015 ◽  
Vol 2015 ◽  
pp. 1-3 ◽  
Author(s):  
Ming-Chi Lu ◽  
Hsing-Chung Ho ◽  
Chen-An Chan ◽  
Chia-Ju Liu ◽  
Jiann-Shing Lih ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Hilbert Transform ◽  
Phase Synchronization ◽  
Nonlinear Dynamical Systems ◽  
The State ◽  
Nonlinear Dynamical ◽  
Large Correlation ◽  
Current Usage ◽  
The Hilbert Transform

We investigate the interplay between phase synchronization and amplitude synchronization in nonlinear dynamical systems. It is numerically found that phase synchronization intends to be established earlier than amplitude synchronization. Nevertheless, amplitude synchronization (or the state with large correlation between the amplitudes) is crucial for the maintenance of a high correlation between two time series. A breakdown of high correlation in amplitudes will lead to a desynchronization of two time series. It is shown that these unique features are caused essentially by the Hilbert transform. This leads to a deep concern and criticism on the current usage of phase synchronization.

Biological Time Series Analysis Using Complex Demodulation

2020 ◽  
pp. 429-457
Author(s):  
Daniel P. Redmond ◽  
Helen C. Sing ◽  
Frederick W. Hegge
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Biological Time ◽  
Complex Demodulation ◽  
Series Analysis

Nonlinear forecasting of non-uniform chaotic attractors in an enzyme reaction

1994 ◽  
Vol 348 (1688) ◽  
pp. 421-430
Keyword(s):  
Time Series ◽  
Enzyme Reaction ◽  
Experimental Conditions ◽  
Biological Time ◽  
Alternative Means ◽  
Nonlinear Forecasting ◽  
Linear Predictors ◽  
Short Term Forecasting ◽  
Low Dimensional ◽  
Frequent Sampling

Nonlinear forecasting was used to predict the time evolution of fluctuating concentrations of dissolved oxygen in the peroxidase-oxidase reaction. This reaction entails the oxidation of NADH with molecular oxygen as the electron acceptor. Depending upon the experimental conditions, either regular or highly irregular oscillations obtain. Previous work suggests that the latter fluctuations are almost certainly chaotic. In either case, the dynamics contain multiple timescales, which fact results in an uneven distribution of points in the phase space. Such ‘nonuniformity,’ as it is called, is a rock on which conventional methods for analysing chaotic time series often founder. The results of the present study are as follows. 1. Short-term forecasting with local linear predictors yields results that are consistent with a hypothesis of low-dimensional chaos. 2. Most of the evidence for nonlinear determinism disappears upon the addition of small amounts of observational error. 3. It is essentially impossible to make predictions over time intervals longer than the average period of oscillation for time series subject to continuous and frequent sampling. 4. Far more effective forecasting is possible for points on Poincare sections. 5. An alternative means for improving forecasting efficacy using the continuous data is to include a second variable (NADH concentration) in the analysis. Since non-uniformity is common in biological time series, we conclude that the application of nonlinear forecasting to univariate time series requires care both in implementation and interpretation.

scholarly journals Learning in Feedforward Neural Networks Accelerated by Transfer Entropy

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 102 ◽  
Author(s):  
Adrian Moldovan ◽  
Angel Caţaron ◽  
Răzvan Andonie
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Information Transfer ◽  
Feedforward Neural Networks ◽  
Theoretical Method ◽  
Transfer Entropy ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Neural Connections ◽  
Feedback Connections

Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural networks. The transfer entropy (TE) was initially introduced as an information transfer measure used to quantify the statistical coherence between events (time series). Later, it was related to causality, even if they are not the same. There are only few papers reporting applications of causality or TE in neural networks. Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks. The information transfer is measured by the TE of feedback neural connections. Intuitively, TE measures the relevance of a connection in the network and the feedback amplifies this connection. We introduce a backpropagation type training algorithm that uses TE feedback connections to improve its performance.

Detecting Determinism in Human Posture Control Data

1998 ◽  
Vol 08 (01) ◽  
pp. 179-188 ◽  
Author(s):  
L. Y. Cao ◽  
B. G. Kim ◽  
J. Kurths ◽  
S. Kim
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Nonstationary Time Series ◽  
Posture Control ◽  
Nonlinear Prediction ◽  
Control Data ◽  
Human Posture ◽  
Prediction Techniques

In this paper, determinism in human posture control data is investigated using the approach of nonlinear prediction. We first comment that one should be cautious of using some statistical methods to analyze nonstationary time series. Then we test the predictability of the human posture control data with different prediction techniques, and investigate how nonstationarity and noise affect the prediction results. Different time series are tested, including data from healthy and ill persons, and different predictabilities are found in different time series.

scholarly journals Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1124
Author(s):  
Payam Shahsavari Baboukani ◽  
Carina Graversen ◽  
Emina Alickovic ◽  
Jan Østergaard
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Difficult Case ◽  
Nonlinear Prediction ◽  
False Detection ◽  
Uniform Embedding ◽  
Embedding Technique ◽  
New Information ◽  
Highly Correlated ◽  
Intracranial Electroencephalography

We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data.

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