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Multifractal analysis for cumulant-based epileptic seizure detection in eeg time series

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Abstract

Multifractal analysis allows us to study scale invariance and fluctuations of the pointwise regularity of time series. A theoretically well grounded multifractal formalism, based on wavelet leaders, was applied to electroencephalogra-phy (EEG) time series measured in healthy volunteers and epilepsy patients, provided by the University of Bonn. We show that the multifractal spectrum during a seizure indicates a lower global regularity when compared to non-seizure data and that multifractal features, combined with few baseline features, can be used to train a supervised learning algorithm to discriminate well above chance ictal (i.e. seizure) versus healthy and interictal epochs (97 %) and healthy controls versus patients (92 %).
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Dates and versions

hal-02108099 , version 1 (24-04-2019)

Identifiers

  • HAL Id : hal-02108099 , version 1

Cite

Omar D Domingues, Philippe Ciuciu, Daria La Rocca, Patrice Abry, Herwig Wendt. Multifractal analysis for cumulant-based epileptic seizure detection in eeg time series. ISBI 2019 - IEEE International Symposium on Biomedical Imaging, Apr 2019, Venise, Italy. ⟨hal-02108099⟩
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