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

Detection of seizures from small samples using nonlinear dynamic system theory

I Yaylali1, H Koçak, P Jayakar

  • 1Miami Children's Hospital, Department of Neuroscience, FL 33155, USA. yaylali@cs.cs.miami.edu

IEEE Transactions on Bio-Medical Engineering
|July 1, 1996
PubMed
Summary

This study quantifies electroencephalogram (EEG) nonlinear dynamics using correlation dimensions (D2) to detect seizure activity. The method reliably identified various seizure types in clinical data.

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

Living donor kidney transplantation after desensitization in cross-match positive high sensitized patients.

Hippokratia·2022
Same author

Expression Profile of MicroRNA Biogenesis Components in Renal Transplant Patients.

Transplantation proceedings·2017
Same author

Effects of hepatitis B surface antigen (HBsAg) positivity of donors in HBsAg(+) renal transplant recipients: comparison of outcomes with HBsAg(+) and HBsAg(-) donors.

Transplant infectious disease : an official journal of the Transplantation Society·2015
Same author

A Successful Renal Transplantation Case After Stem Cell Transplantation.

Transplantation proceedings·2015
Same author

Outcomes of Renal Transplantation in Patients With Alport Syndrome.

Transplantation proceedings·2015
Same author

Comparative reliability analysis of publicly available software packages for automatic intracranial volume estimation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2015

Area of Science:

  • Neuroscience
  • Nonlinear Dynamics
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) signals exhibit nonlinear dynamics.
  • Correlation dimension (D2) quantifies these dynamics in time series data.
  • Detecting electrographic seizure activity is crucial for neurological disorder diagnosis.

Purpose of the Study:

  • To utilize D2 of the unbiased autocovariance function of EEG data for electrographic seizure detection.
  • To enhance the reliability of D2 computations on short-duration EEG data.

Main Methods:

  • Acquired digital EEG data at 200 Hz, organized into 2.56-second frames.
  • Applied unbiased autocovariance analysis to simplify raw EEG and highlight periodic seizure activity.
  • Computed D2 from the autocovariance function using the Grassberger-Procaccia method with Theiler's box-assisted algorithm.

Related Experiment Videos

Main Results:

  • The D2 computation from the autocovariance function proved computationally robust for short EEG data.
  • The method showed no significant sensitivity to implementation parameters like embedding dimension and box size.
  • The developed system successfully identified various seizure types in clinical studies.

Conclusions:

  • D2 analysis of the unbiased autocovariance function is a reliable method for electrographic seizure detection.
  • This approach offers computational robustness and insensitivity to parameter choices, making it suitable for clinical application.
  • The findings support the use of nonlinear dynamics in EEG analysis for improved seizure identification.