A Neural Networks Approach to EEG Signals Modeling

Hasan A. Al-Nashash, Ali M.S. Zalzala, Nitish V. Thakor

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, a comparison of the application of neural networks and a first order Markov process amplitude model are reported for the modelling of electoencephalography (EEG) signals recorded from a controlled experimental setup of rodent brain injury with hypoxic-ischemic cardiac arrest. The NN model was found to be superior in modeling the nonlinearities of EEG signal variations.

Original languageEnglish (US)
Pages (from-to)2451-2454
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003

Keywords

  • Brain Injury
  • Cardiac Arrest
  • EEG
  • Markov
  • Modeling
  • Neural Networks

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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