EEG Signal Modeling Using Adaptive Markov Process Amplitude

Hasan Al-Nashash, Yousef Al-Assaf, Joseph Paul, Nitish V Thakor

Research output: Contribution to journalArticle

Abstract

In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysfological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order Markov process model. EEG signals recorded from rodent brains during injury and recovery following global cerebral ischemia are utilized as input signals to the model. The EEG was recorded in a controlled experimental brain injury model of hypoxic-ischemic cardiac arrest. The signals from the injured brain during various phases of injury and recovery were modeled. Results show that the adaptive model is accurate in simulating EEG signal variations following brain injury. The dynamics of the model coefficients successfully capture the presence of spiking and bursting in EEG.

Original languageEnglish (US)
Pages (from-to)744-751
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume51
Issue number5
DOIs
StatePublished - May 2004

Fingerprint

Electroencephalography
Markov processes
Brain
Brain models
Recovery

Keywords

  • Cardiac arrest
  • EEG
  • Markov process
  • Signal modeling

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

EEG Signal Modeling Using Adaptive Markov Process Amplitude. / Al-Nashash, Hasan; Al-Assaf, Yousef; Paul, Joseph; Thakor, Nitish V.

In: IEEE Transactions on Biomedical Engineering, Vol. 51, No. 5, 05.2004, p. 744-751.

Research output: Contribution to journalArticle

Al-Nashash, Hasan ; Al-Assaf, Yousef ; Paul, Joseph ; Thakor, Nitish V. / EEG Signal Modeling Using Adaptive Markov Process Amplitude. In: IEEE Transactions on Biomedical Engineering. 2004 ; Vol. 51, No. 5. pp. 744-751.
@article{441bd83c840a4346ada54b9475ac601e,
title = "EEG Signal Modeling Using Adaptive Markov Process Amplitude",
abstract = "In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysfological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order Markov process model. EEG signals recorded from rodent brains during injury and recovery following global cerebral ischemia are utilized as input signals to the model. The EEG was recorded in a controlled experimental brain injury model of hypoxic-ischemic cardiac arrest. The signals from the injured brain during various phases of injury and recovery were modeled. Results show that the adaptive model is accurate in simulating EEG signal variations following brain injury. The dynamics of the model coefficients successfully capture the presence of spiking and bursting in EEG.",
keywords = "Cardiac arrest, EEG, Markov process, Signal modeling",
author = "Hasan Al-Nashash and Yousef Al-Assaf and Joseph Paul and Thakor, {Nitish V}",
year = "2004",
month = "5",
doi = "10.1109/TBME.2004.826602",
language = "English (US)",
volume = "51",
pages = "744--751",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",
number = "5",

}

TY - JOUR

T1 - EEG Signal Modeling Using Adaptive Markov Process Amplitude

AU - Al-Nashash, Hasan

AU - Al-Assaf, Yousef

AU - Paul, Joseph

AU - Thakor, Nitish V

PY - 2004/5

Y1 - 2004/5

N2 - In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysfological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order Markov process model. EEG signals recorded from rodent brains during injury and recovery following global cerebral ischemia are utilized as input signals to the model. The EEG was recorded in a controlled experimental brain injury model of hypoxic-ischemic cardiac arrest. The signals from the injured brain during various phases of injury and recovery were modeled. Results show that the adaptive model is accurate in simulating EEG signal variations following brain injury. The dynamics of the model coefficients successfully capture the presence of spiking and bursting in EEG.

AB - In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysfological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order Markov process model. EEG signals recorded from rodent brains during injury and recovery following global cerebral ischemia are utilized as input signals to the model. The EEG was recorded in a controlled experimental brain injury model of hypoxic-ischemic cardiac arrest. The signals from the injured brain during various phases of injury and recovery were modeled. Results show that the adaptive model is accurate in simulating EEG signal variations following brain injury. The dynamics of the model coefficients successfully capture the presence of spiking and bursting in EEG.

KW - Cardiac arrest

KW - EEG

KW - Markov process

KW - Signal modeling

UR - http://www.scopus.com/inward/record.url?scp=1942456672&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1942456672&partnerID=8YFLogxK

U2 - 10.1109/TBME.2004.826602

DO - 10.1109/TBME.2004.826602

M3 - Article

C2 - 15132500

AN - SCOPUS:1942456672

VL - 51

SP - 744

EP - 751

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 5

ER -