Learning Vector Quantization and Permutation Entropy to analyse epileptic electroencephalography

Nadia Mammone, Jonas Duun-Henriksen, Troels Wesenberg Kjr, Maurizio Campolo, Fabio La Foresta, Francesco C. Morabito

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


In this paper, we address the issue of dealing with huge amounts of data from recordings of an Electroencephalogram (EEG) in epileptic patients. In particular, the attention is focused on the development of tools to support the neurophysiologists in the time consuming and challenging task of reviewing the EEG to identify critical events that are worth of inspection for diagnostic purposes. A novel methodology is proposed for the automatic estimation of descriptors of EEG complexity and the subsequent classification of critical events. Based on the estimation of Permutation Entropy (PE) profiles from the EEG traces, the methodology relies on Learning Vector Quantization (LVQ) to cluster the electrodes in a competitive way according to their PE levels and to classify the cerebral state accordingly. An absence seizure EEG of 15.5 minutes was processed and a 93.94% sensitivity together with a 100% specificity were obtained.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479919604, 9781479919604, 9781479919604, 9781479919604
Publication statusPublished - Sep 28 2015
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: Jul 12 2015Jul 17 2015


OtherInternational Joint Conference on Neural Networks, IJCNN 2015


  • Electroencephalography
  • Logic gates
  • Neurons

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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