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

Time-Varying Time-Frequency Complexity Measures for Epileptic EEG Data Analysis.

Marcelo A Colominas, Mohamad El Sayed Hussein Jomaa, Nisrine Jrad

    IEEE Transactions on Bio-Medical Engineering
    |October 14, 2017
    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

    Impact of simultaneous motor-cognitive training on motor capacities in older adults: A quasi-randomized parallel controlled trial.

    Experimental gerontology·2026
    Same author

    Pediatric acute stroke alert in Nantes and Angers hospitals: description of patients included and comparison with published data.

    Archives de pediatrie : organe officiel de la Societe francaise de pediatrie·2025
    Same author

    Unsupervised Deep Embedding for Robust Epileptic Seizure Detection.

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
    Same author

    Enhanced Brain-Heart Connectivity as a Precursor of Reduced State Anxiety after Therapeutic Virtual Reality Immersion.

    Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
    Same author

    HCN2-Associated Neurodevelopmental Disorders: Data from Patients and Xenopus Cell Models.

    Annals of neurology·2025
    Same author

    Intracranial hypertension in a patient with Hutchinson-Gilford progeria syndrome.

    Archives de pediatrie : organe officiel de la Societe francaise de pediatrie·2025
    Same journal

    Assessment of skin stiffness in systemic sclerosis using optical coherence elastography: A comparative study with histology and clinical parameters.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer and Their Caregivers.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    A Kalman Filter-Based Framework for Granger Causality Assessment: Application in Tracking Maternal-Fetal Heart Rate Coupling.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

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

    New time-frequency entropy measures objectively evaluate epilepsy treatment effectiveness using resting-state electroencephalography (EEG) signals. Increased signal complexity indicates patient improvement, aiding in treatment monitoring and classification.

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Epilepsy treatment monitoring relies on objective measures.
    • Resting-state electroencephalography (EEG) offers insights into brain activity.
    • Time-frequency entropy measures can quantify signal complexity.

    Purpose of the Study:

    • To evaluate epilepsy treatment using novel time-frequency entropy measures.
    • To assess resting-state EEG signal complexity changes post-medication.
    • To introduce and validate new time-varying complexity measures.

    Main Methods:

    • Reviewed Rényi entropy and singular value decomposition-based entropy in time-frequency domains.
    • Introduced time-varying versions of these entropy measures.

    Related Experiment Videos

  • Applied measures to synthetic and real EEG data, using Friedman tests and PCA for analysis.

    • Main Results:

    • Consistent increase in complexity measures across brain regions observed.
    • Extracted features demonstrated utility in monitoring treatment efficacy.
    • Combined features achieved high classification accuracy (AUC > 0.93).

    Conclusions:

    • Time-frequency complexity measures effectively monitor epilepsy treatment.
    • Introduced time-varying complexity measures offer new analytical tools.
    • These methods show promise for patient classification and broader applications.