Epileptic seizure prediction from eigen-wavelet multivariate selfsimilarity analysis of multi-channel EEG signals - Mines Saint-Étienne Access content directly
Conference Papers Year : 2023

Epileptic seizure prediction from eigen-wavelet multivariate selfsimilarity analysis of multi-channel EEG signals

Abstract

Epileptic patients may suffer from severe brain damages during seizures. There is thus a significant need for automated seizure prediction. Independently, brain macroscopic activity has been shown to display scalefree temporal dynamics, which, in turn, were involved into seizure prediction. Selfsimilarity, the paradigm model for scalefree dynamics, has however mostly been defined in univariate settings, thus yielding a collection of independent analyses of recorded signals. Yet, nonnegligible correlations exist in multi-channel recordings of brain activity and may prove useful in seizure prediction. This work aims to assess the benefits of using a recently developed multivariate eigen-wavelet framework for multivariate selfsimilarity analysis in seizure prediction using CHB-MIT Scalp EEG data.
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Dates and versions

emse-04216653 , version 1 (25-09-2023)

Identifiers

  • HAL Id : emse-04216653 , version 1

Cite

Charles-Gérard Lucas, Patrice Abry, Herwig Wendt, Gustavo Didier. Epileptic seizure prediction from eigen-wavelet multivariate selfsimilarity analysis of multi-channel EEG signals. 31st European Signal Processing Conference (EUSICO 2023), Sep 2023, Helsinki, Finland. à paraître. ⟨emse-04216653⟩
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