Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

EEG signal modeling using adaptive Markov process amplitude.

Hasan Al-Nashash1, Yousef Al-Assaf, Joseph Paul

  • 1School of Engineering, American University of Sharjah, Sharjah, UAE.

IEEE Transactions on Bio-Medical Engineering
|May 11, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Single-Pixel Tactile Skin via compressive sampling.

Communications engineering·2026
Same author

Rapid plasticity of default-mode local network architectures following adult-onset blindness.

Cell reports·2026
Same author

A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation.

Scientific reports·2025
Same author

Development and characterization of novel flexible cellulose electrodes for electrophysiological monitoring.

RSC advances·2025
Same author

Investigating the dynamics of intracranial pressure and cerebral autoregulation during extracorporeal cardiopulmonary resuscitation using a porcine model.

Resuscitation plus·2025
Same author

Preserving Early Neurovascular Responses: The Effect of tACS on the Hemodynamics in Stressed Individuals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same journal

Magnetic Resonance Spectroscopy Deep Learning with Magnetic Resonance Background Generator Enables In Vivo Metabolite Quantification of Hepatic Encephalopathy.

IEEE transactions on bio-medical engineering·2026
Same journal

Use of RPNIs and Implanted Electrodes for Prosthetic Wrist and Multi-Grip Hand Control during Functional Tasks: A Case Study.

IEEE transactions on bio-medical engineering·2026
Same journal

Healthy Limb Driven Prediction for Real Time Control of Unilateral Exoskeletons in Gait Rehabilitation.

IEEE transactions on bio-medical engineering·2026
Same journal

A Miniature Wearable Ultrasound System for Continuous Bladder Monitoring with Sleeping-Position-Robust Modeling Strategies.

IEEE transactions on bio-medical engineering·2026
Same journal

A Bi-objective Array Optimization Framework for Magnetocardiographic Source Imaging.

IEEE transactions on bio-medical engineering·2026
Same journal

A Dynamic Mutual Information Measure of Phase-Amplitude Coupling with Uncertainty Quantification.

IEEE transactions on bio-medical engineering·2026
See all related articles

This study models electroencephalogram (EEG) signals using an adaptive Markov process to detect brain injury changes. The model accurately simulates EEG variations during injury and recovery, aiding potential clinical diagnosis.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Electroencephalogram (EEG) signal analysis is crucial for understanding brain function and diagnosing neurological disorders.
  • Pathophysiological changes in EEG can indicate brain injury, but accurate modeling is challenging.
  • Global cerebral ischemia, often resulting from cardiac arrest, causes significant brain injury with detectable EEG alterations.

Purpose of the Study:

  • To develop and validate an adaptive Markov process amplitude algorithm for modeling and simulating electroencephalogram (EEG) signals.
  • To assess the utility of EEG signal modeling in identifying pathophysiological changes associated with brain injury.
  • To investigate the dynamics of EEG signals during injury and recovery phases in a rodent model.

Main Methods:

Related Experiment Videos

  • Utilized an adaptive Markov process amplitude algorithm to model EEG signals.
  • Employed the least mean square algorithm for continuous estimation of first-order Markov process model parameters.
  • Applied the model to EEG data recorded from rodent brains during hypoxic-ischemic cardiac arrest and subsequent recovery.

Main Results:

  • The adaptive model demonstrated high accuracy in simulating EEG signal variations following brain injury.
  • Model coefficient dynamics successfully captured the presence of spiking and bursting patterns characteristic of injured brain activity.
  • The simulation results correlated well with the observed EEG changes during different phases of injury and recovery.

Conclusions:

  • Adaptive Markov process modeling provides an accurate method for simulating EEG signals in the context of brain injury.
  • This modeling approach can effectively identify and characterize pathophysiological EEG changes, offering potential for clinical diagnostic tools.
  • The study highlights the capability of computational models to capture complex neural dynamics during critical brain events.