A Neural Networks Approach to EEG Signals Modeling

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

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

5 Citations (Scopus)

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
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages2451-2454
Number of pages4
Volume3
Publication statusPublished - 2003
EventA New Beginning for Human Health: the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: 17 Sep 200321 Sep 2003

Conference

ConferenceA New Beginning for Human Health: the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CountryMexico
CityCancun
Period17/09/0321/09/03

Keywords

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

Fingerprint

Dive into the research topics of 'A Neural Networks Approach to EEG Signals Modeling'. Together they form a unique fingerprint.

Cite this