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 Video

Updated: Jul 17, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
05:58

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

38.9K

Automated diagnosis of EEG abnormalities with different classification techniques.

Essam Abdellatef1, Heba M Emara2, Mohamed R Shoaib3

  • 1Department of Electronics and Communications, Delta Higher Institute for Engineering and Technology (DHIET), 35511, Mansoura, Egypt.

Medical & Biological Engineering & Computing
|September 6, 2023
PubMed
Summary

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

Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications.

Scientific reports·2026
Same author

A hybrid transformer-zero-shot learning framework with Muon optimization for intelligent channel estimation in MIMO wireless systems.

Scientific reports·2026
Same author

Automated diabetic retinopathy classification using vision transformers on optical confocal microscopy images.

Applied optics·2026
Same author

Recovery in bone mineral density following recent repeat pregnancy and breastfeeding among African women with and without HIV.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2026
Same author

High performance IP lookup through GPU acceleration to support scalable and efficient routing in data driven communication networks.

Scientific reports·2026
Same author

PCSA-Net: pyramid channel and spatial attention network for multiclass renal disease diagnosis using CT images.

Scientific reports·2026

This study introduces two advanced methods for automatic seizure detection and prediction from EEG data. Both machine learning and deep learning approaches achieved high accuracy, outperforming existing techniques.

Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Signal Processing

Background:

  • Epileptic seizure detection and prediction from Electroencephalograms (EEGs) are complex due to low signal-to-noise ratios, patient-specific seizure variations, and data limitations.
  • Existing methods face challenges in accurately identifying and forecasting seizures in clinical settings.

Purpose of the Study:

  • To develop and evaluate two novel approaches for enhanced automatic seizure detection and prediction using EEG signals.
  • To overcome the limitations of current methods by employing advanced signal processing and machine learning techniques.

Main Methods:

  • Developed a Machine Learning (ML) model utilizing features from the Hilbert Marginal Spectrum (HMS) domain, including entropy, higher-order statistics, and sub-band energies, classified with SVM, LR, and KNN.
Keywords:
EEGEMDEpilepsyHMSSeizure detectionSeizure prediction

More Related Videos

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.2K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.4K

Related Experiment Videos

Last Updated: Jul 17, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
05:58

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

38.9K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.2K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.4K
  • Implemented a deep learning approach using Convolutional Neural Networks (CNNs) with residual learning on EEG spectrograms for seizure detection, prediction, and 3-state classification.

    • Main Results:

    • The HMS-based ML model achieved 100% accuracy for seizure detection and prediction.
    • The CNN-based model demonstrated high accuracies: 97.66% (Seizure vs. Pre-Seizure), 95.59% (Non-Seizure vs. Seizure), and 94.51% (Non-Seizure vs. Seizure vs. Pre-Seizure).
    • Both methods outperformed state-of-the-art techniques on the CHB-MIT EEG dataset.

    Conclusions:

    • The proposed HMS-based and CNN-based approaches are highly effective for automatic epileptic seizure detection and prediction.
    • These methods offer significant improvements over existing techniques, paving the way for more reliable seizure monitoring.
    • The study highlights the potential of advanced computational methods in improving epilepsy management.