Wavelet entropy method for EEG analysis: application to global brain injury

H. A. Al-Nashash, J. S. Paul, N. V. Thakor

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

Abstract

A novel method, "wavelet entropy," is presented for the analysis of EEG signals recorded during injury and recovery following global cerebral ischemia. The EEG is recorded from rodent brains in a controlled experimental brain injury model by hypoxic-ischemic cardiac arrest. Wavelet analysis is used to decompose the EEG into standard clinical subbands. Entropy is then computed using these wavelet coefficients. The wavelet entropy helps segment periods of bursting in EEG signals. The residual entropy of the wavelet coefficients is also computed. It reflects the degree of synchronization of the brain rhythm generators.

Original languageEnglish (US)
Title of host publicationConference Proceedings - 1st International IEEE EMBS Conference on Neural Engineering
EditorsLaura J. Wolf, Jodi L. Strock
PublisherIEEE Computer Society
Pages348-351
Number of pages4
ISBN (Electronic)0780375793
DOIs
StatePublished - Jan 1 2003
Event1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
Duration: Mar 20 2003Mar 22 2003

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2003-January
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other1st International IEEE EMBS Conference on Neural Engineering
CountryItaly
CityCapri Island
Period3/20/033/22/03

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Keywords

  • Brain injuries
  • Brain modeling
  • Cardiac arrest
  • Electroencephalography
  • Entropy
  • Ischemic pain
  • Rodents
  • Signal analysis
  • Wavelet analysis
  • Wavelet coefficients

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

Cite this

Al-Nashash, H. A., Paul, J. S., & Thakor, N. V. (2003). Wavelet entropy method for EEG analysis: application to global brain injury. In L. J. Wolf, & J. L. Strock (Eds.), Conference Proceedings - 1st International IEEE EMBS Conference on Neural Engineering (pp. 348-351). [1196832] (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2003-January). IEEE Computer Society. https://doi.org/10.1109/CNE.2003.1196832