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 background activity in childhood epilepsy: valid parameterisation by the partial autocorrelation function.

A Gundel

    Neuropediatrics
    |August 1, 1983
    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

    Melaleuca alternifolia nanoparticles against Candida species biofilms.

    Microbial pathogenesis·2017
    Same author

    Surface micro-morphology, phase transformation, and mechanical reliability of ground and aged monolithic zirconia ceramic.

    Journal of the mechanical behavior of biomedical materials·2016
    Same author

    Single-step electrochemical nanolithography of metal thin films by localized etching with an AFM tip.

    Nanotechnology·2011
    Same author

    Task-dependent differences in subjective fatigue scores.

    Journal of sleep research·2005
    Same author

    Human thermohomeostasis onboard "Mir" and in simulated microgravity studies.

    Acta astronautica·2001
    Same author

    A circadian oscillator model based on empirical data.

    Journal of biological rhythms·2000

    Electroencephalogram (EEG) background activity patterns differ significantly between favorable and unfavorable courses of primary myoclonic-astatic epilepsy. This finding aids in predicting epilepsy progression using EEG data.

    Area of Science:

    • Neurology
    • Epileptology
    • Biomedical Engineering

    Background:

    • Primary myoclonic-astatic epilepsy presents with variable clinical courses.
    • Understanding predictors of disease progression is crucial for patient management.
    • Electroencephalogram (EEG) background activity is a potential biomarker.

    Purpose of the Study:

    • To investigate the relationship between EEG background activity and the clinical course of primary myoclonic-astatic epilepsy.
    • To identify EEG parameters that differentiate between favorable and unfavorable disease trajectories.

    Main Methods:

    • Analysis of EEG background activity using partial autocorrelation function in 42 patients.
    • Application of variance analysis, linear discriminant analysis, and factor analysis.

    Related Experiment Videos

  • Classification of patients into favorable and unfavorable clinical course groups based on seizure occurrence.
  • Main Results:

    • Significant differences in EEG background activity were found between the two clinical groups.
    • A discriminant factor, termed rhythm-power-factor, was identified.
    • Unfavorable course associated with theta-band rhythms and higher power; favorable course with alpha-band rhythms.

    Conclusions:

    • EEG background activity, particularly the rhythm-power-factor, is a significant predictor of clinical course in primary myoclonic-astatic epilepsy.
    • Hypersynchronous potentials did not correlate with the clinical course.
    • EEG background activity provides valuable information regarding convulsibility in this epilepsy type.