STLmax joint mutual information for quantifying independence in the epileptic brain

Nadia Mammone, Fabio La Foresta, Francesco C. Morabito, Mario Versaci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Results in literature show that the convergence of the Short-Term Maximum Lyapunov Exponent (STLmax) time series, extracted from intracranial EEG recorded from patients affected by intractable temporal lobe epilepsy, is linked to the seizure onset. When the STLmax profiles of different electrode sites converge (high entrainment) a seizure is likely to occur. In this paper Renyi's Mutual information (MI) is introduced in order to investigate the independence between pairs of electrodes involved in the epileptogenesis. A scalp EEG recording and an intracranial EEG recording, including two seizures each, were analysed. STLmax was estimated for each critical electrode and then MI between couples of STLmax profiles was measured. MI showed sudden spikes that occurred 8 to 15 min before the seizure onset. Thus seizure onset appears related to a burst in MI: this suggests that seizure development might restore the independence between STLmax of critical electrode sites.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages30-39
Number of pages10
Volume193
Edition1
ISBN (Print)9781586039844
DOIs
Publication statusPublished - 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
Number1
Volume193
ISSN (Print)09226389

Keywords

  • Chaos Theory
  • Electroencephalography
  • Mutual Information
  • Short-Term Maximum Lyapunov Exponent

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'STLmax joint mutual information for quantifying independence in the epileptic brain'. Together they form a unique fingerprint.

  • Cite this

    Mammone, N., La Foresta, F., Morabito, F. C., & Versaci, M. (2009). STLmax joint mutual information for quantifying independence in the epileptic brain. In Frontiers in Artificial Intelligence and Applications (1 ed., Vol. 193, pp. 30-39). (Frontiers in Artificial Intelligence and Applications; Vol. 193, No. 1). IOS Press. https://doi.org/10.3233/978-1-58603-984-4-30