EEG signal segmentation using adaptive Markov process amplitude modeling

Yousef M. Al-Assaf, Hasan A. Al-Nashash, Joseph S. Paul, Nitish V. Thakor

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, adaptive Markov process amplitude modeling was used to simulate and segment EEG signals. The least mean square adaptive algorithm was used to estimate the parameters of a first order Markov model. The coefficients of the model were utilized for EEG signal segmentation. The EEG signals were recorded from a controlled experimental setup of rodent brain injury with hypoxic-ischemic cardiac arrest. Results demonstrated that the proposed technique is a potential tool for EEG signal analysis.

Original languageEnglish (US)
Pages (from-to)173-174
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
StatePublished - Dec 1 2002
Externally publishedYes
EventProceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States
Duration: Oct 23 2002Oct 26 2002

ASJC Scopus subject areas

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

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