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EEG signal segmentation using adaptive Markov process amplitude modeling

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4 Author(s)
Al-Assaf, Y.M. ; Sch. of Eng., American Univ. of Sharjah, United Arab Emirates ; Al-Nashash, H.A. ; Paul, J.S. ; Thakor, N.V.

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.

Published in:
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint  (Volume:1 )

Date of Conference: 2002

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