Study of Learning Entropy for onset detection of epileptic seizures in EEG time series

Ivo Bukovsky, Matous Cejnek, Jan Vrba, Noriyasu Homma

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

5 Citations (Scopus)

Abstract

This paper presents a case study of non-Shannon entropy, i.e. Learning Entropy (LE), for instant detection of onset of epileptic seizures in individual EEG time series. Contrary to entropy methods of EEG evaluation that are based on probabilistic computations, we present the LE-based approach that evaluates the conformity of individual samples of data to the contemporary learned governing law of a learning system and thus LE can detect changes of dynamics on individual samples of data. For comparison, the principle and the results are compared to the Sample Entropy approach. The promising results indicate the LE potentials for feature extraction enhancement for early detection of epileptic seizures on individual-data-sample basis.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3302-3305
Number of pages4
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 2016 Oct 31
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 2016 Jul 242016 Jul 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Other

Other2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

Keywords

  • Adaptive novelty detection
  • EEG time series
  • Epileptic seizure
  • Higher order neural units
  • Incremental learning
  • Learning Entropy
  • Non-Shannon entropy
  • Onset detection
  • Sample Entropy

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Study of Learning Entropy for onset detection of epileptic seizures in EEG time series'. Together they form a unique fingerprint.

  • Cite this

    Bukovsky, I., Cejnek, M., Vrba, J., & Homma, N. (2016). Study of Learning Entropy for onset detection of epileptic seizures in EEG time series. In 2016 International Joint Conference on Neural Networks, IJCNN 2016 (pp. 3302-3305). [7727621] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2016.7727621