On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Lerche, E.; Garzotti, L.; Gelfusa, M.; Contributors, Jet

doi:10.3390/e20090627

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…Furthermore, additional formulations, more suited to the investigation of actual causal relations than simple correlations, are also under consideration [23][24][25][26]. In terms of practical applications, some of the most immediate range from the investigation of synchronization experiments and disruptions in thermonuclear fusion [27][28][29][30][31][32][33][34][35][36][37] to the refinement of measurement techniques…”

Section: Discussionmentioning

confidence: 99%

The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio

et al. 2021

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Understanding and quantifying the mutual influence between systems remain crucial but challenging tasks in any scientific enterprise. The Pearson correlation coefficient, the mutual information, and the information quality ratio are the most widely used indicators, only the last two being valid for nonlinear interactions. Given their limitations, a new criterion is proposed, the reciprocal influence criterion, which is very simple conceptually and does not make any assumption about the statistics of the stochastic variables involved. In addition to being normalised as the information quality ratio, it provides a much better resilience to noise and much higher stability to the issues related to the determination of the involved probability distribution functions. A conditional version, to counteract the effects of confounding variables, has also been developed, showing the same advantages compared to the more traditional indicators. A series of systematic tests with numerical examples is reported, to compare the properties of the new indicator with the more traditional ones, proving its clear superiority in practically all respects.

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Section: Discussionmentioning

confidence: 99%

The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio

et al. 2021

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“…H(Y|Y − ) is the conditional entropy of Y given its own past, and H(Y|X − , Y − ) is the conditional entropy of Y given its own past and the past of X. Given this dependency on the past observations of both processes, TE is an asymmetric measure, which constitutes an advantage when quantifying the directionality of the information transfer from one system to the other [11,30].…”

Section: Transfer Entropymentioning

confidence: 99%

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

Rozo

Morales

Moeyersons

et al. 2021

Entropy

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Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

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“…A second typology of test has been performed, aimed at investigating the causality horizon [ 26 , 27 , 28 , 29 ], i.e., the maximum time interval into which two physical quantities are synchronized and in which one observable can be thought as the “drive mechanism” of the second observable, for periodic and quasiperiodic signals of the same nature as those encountered in real experiments in thermonuclear scenarios [ 30 , 31 ]. In this kind of experiment, particular instabilities of the plasma, with potential harmful effects on the machine integrity, are paced by triggering them frequently enough that they do not have time to become dangerous for either the performance or safety of the reactor.…”

Section: Numerical Tests: Causality Horizonmentioning

confidence: 99%

A Refinement of Recurrence Analysis to Determine the Time Delay of Causality in Presence of External Perturbations

Peluso

Murari

2020

Entropy

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This article describes a refinement of recurrence analysis to determine the delay in the causal influence between a driver and a target, in the presence of additional perturbations affecting the time series of the response observable. The methodology is based on the definition of a new type of recurrence plots, the Conditional Joint Recurrence plot. The potential of the proposed approach resides in the great flexibility of recurrence plots themselves, which allows extending the technique to more than three quantities. Autoregressive time series, both linear and nonlinear, with different couplings and percentage of additive Gaussian noise have been investigated in detail, with and without outliers. The approach has also been applied to the case of synthetic periodic signals, representing realistic situations of synchronization experiments in thermonuclear fusion. The results obtained have been very positive; the proposed Conditional Joint Recurrence plots have always managed to identify the right interval of the causal influences and are very competitive with alternative techniques such as the Conditional Transfer Entropy.

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On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

Cited by 15 publications

References 23 publications

The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio

The Reciprocal Influence Criterion: An Upgrade of the Information Quality Ratio

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

A Refinement of Recurrence Analysis to Determine the Time Delay of Causality in Presence of External Perturbations

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