A Neural Networks Approach to EEG Signals Modeling

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

A Neural Networks Approach to EEG Signals Modeling

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

School of Medicine

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

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 language	English (US)
Pages (from-to)	2451-2454
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	3
State	Published - 2003
Event	A 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 2003 → Sep 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

Cite this

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title = "A Neural Networks Approach to EEG Signals Modeling",

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.",

keywords = "Brain Injury, Cardiac Arrest, EEG, Markov, Modeling, Neural Networks",

author = "Al-Nashash, {Hasan A.} and Zalzala, {Ali M.S.} and Thakor, {Nitish V.}",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 17-09-2003 Through 21-09-2003",

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AU - Al-Nashash, Hasan A.

AU - Zalzala, Ali M.S.

AU - Thakor, Nitish V.

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

KW - Brain Injury

KW - Cardiac Arrest

KW - EEG

KW - Markov

KW - Modeling

KW - Neural Networks

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M3 - Conference article

AN - SCOPUS:1542330938

SN - 0589-1019

VL - 3

SP - 2451

EP - 2454

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 17 September 2003 through 21 September 2003

ER -

A Neural Networks Approach to EEG Signals Modeling

Abstract

Keywords

ASJC Scopus subject areas

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