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 Concept Videos

Seizures: Classification01:13

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
Seizures l: Introduction01:20

Seizures l: Introduction

Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...

You might also read

Related Articles

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

Sort by
Same author

Distinct medication-state modulation of motor-cortical low-beta power in tremor-dominant and postural instability/gait difficulty Parkinson's disease: a source-space resting-state EEG study.

Frontiers in neurology·2026
Same author

HDFT-MViT: a progressive core-enhanced mix framework for Alzheimer's disease classification using MRI images.

Frontiers in neurology·2026
Same author

Quadrant-specific assessment of anterior chamber angles in primary angle-closure suspects using anterior segment-OCT: a cross-sectional observational study.

BMC ophthalmology·2026
Same author

The Endothelial Cell Perspective in Pulmonary Fibrosis: From Cell Fate Decisions, Intercellular Communication, and EndoMT to Emerging Therapies.

Canadian respiratory journal·2026
Same author

Macular microcirculation changes in incident type 2 diabetes without fundus Photography-Detectable diabetic Retinopathy: A 6-Year nested Case-Control study.

Diabetes research and clinical practice·2026
Same author

Legume proteins with a focus on seed fractions: Unraveling structural foundations for health-promoting applications.

Food chemistry·2026

Related Experiment Video

Updated: Jun 25, 2026

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System
06:28

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

Published on: September 27, 2024

Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.

Shunyun Wang1, Jincan Zhang1, Wenna Chen2

  • 1College of Information Engineering, Henan University of Science and Technology, Luoyang, China.

Frontiers in Neuroscience
|June 24, 2026
PubMed
Summary

This study introduces a novel hybrid framework for improved electroencephalogram (EEG)-based seizure prediction. The model achieves high accuracy in identifying preictal states, offering potential for advanced seizure warning systems.

Keywords:
electroencephalographyfeature extractionhybrid modelseizure predictionself-attention

Related Experiment Videos

Last Updated: Jun 25, 2026

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System
06:28

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

Published on: September 27, 2024

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Accurate seizure prediction remains a significant clinical challenge.
  • Electroencephalogram (EEG)-based monitoring is crucial with advancements in smart medical technology.
  • Existing methods struggle to model complex, multiscale EEG characteristics effectively.

Purpose of the Study:

  • To enhance the accuracy of EEG-based seizure prediction.
  • To develop a hybrid framework modeling multiscale EEG features.
  • To improve patient-specific preictal state identification.

Main Methods:

  • EEG signals decomposed into sub-bands using Discrete Wavelet Transform.
  • Extracted time-frequency and nonlinear features fed into a channel-centric model (DC-SA-EBiLSTM).
  • Model integrates depthwise separable convolution, self-attention, and enhanced BiLSTM for feature extraction and sequence modeling.

Main Results:

  • Achieved 95.89% average accuracy, 96.70% sensitivity, 95.48% specificity, and 99.02% AUC.
  • Event-level validation showed 95.96% event sensitivity with a low false alarm rate (0.316 FPR/h).
  • Demonstrated a mean early warning time of 30.52 minutes.

Conclusions:

  • The DC-SA-EBiLSTM framework effectively captures local and global inter-channel EEG dependencies.
  • The approach provides a feature-driven method for patient-specific preictal state prediction.
  • The model shows significant potential for real-world EEG-based seizure prediction systems.