Cross-Scale Causality and Information Transfer in Simulated Epileptic Seizures

An information-theoretic approach for detecting causality and information transfer was applied to phases and amplitudes of oscillatory components related to different time scales and obtained using the wavelet transform from a time series generated by the Epileptor model. Three main time scales and...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2021-04, Vol.23 (5), p.526
Main Authors: Gupta, Kajari, Paluš, Milan
Format: Article
Language:English
Subjects:
Amplitudes
Brain research
Causality
Convulsions & seizures
epilepsy model
Granger causality
Information theory
Information transfer
interactions
multiscale dynamics
Probability distribution
Random variables
Seizures
Time series
Wavelet transforms
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Summary:An information-theoretic approach for detecting causality and information transfer was applied to phases and amplitudes of oscillatory components related to different time scales and obtained using the wavelet transform from a time series generated by the Epileptor model. Three main time scales and their causal interactions were identified in the simulated epileptic seizures, in agreement with the interactions of the model variables. An approach consisting of wavelet transform, conditional mutual information estimation, and surrogate data testing applied to a single time series generated by the model was demonstrated to be successful in the identification of all directional (causal) interactions between the three different time scales described in the model. Thus, the methodology was prepared for the identification of causal cross-frequency phase-phase and phase-amplitude interactions in experimental and clinical neural data.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23050526