Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Seizures: Classification

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:

You might also read

Related Articles

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

Sort by

Same author

Complex temporal network analysis based on the difference visibility graph for epilepsy with and without electrical status epilepticus during sleep (ESES) patients.

Health information science and systems·2026

Same author

Brain Connectivity Yields Insights into the Pathogenesis of Epilepsy and Subtypes: Evidence from Mendelian Randomization Analysis.

Health data science·2025

Same author

Two-Year Hypertension Incidence Risk Prediction in Populations in the Desert Regions of Northwest China: Prospective Cohort Study.

Journal of medical Internet research·2025

Same author

KSDKG: construction and application of knowledge graph for kidney stone disease based on biomedical literature and public databases.

Health information science and systems·2024

Same author

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

Cancer science·2024

Same author

Air Pollution and Diabetes Mellitus: Association and Validation in a Desert Area in China.

The Journal of clinical endocrinology and metabolism·2024

Search research articles

Related Experiment Video

Updated: Jul 18, 2025

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Combining temporal and spatial attention for seizure prediction.

Yao Wang¹, Yufei Shi², Zhipeng He²

¹School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006 Guangdong China.

Health Information Science and Systems

|August 28, 2023

Summary

This summary is machine-generated.

A new Gatformer model improves epilepsy seizure prediction by analyzing spatiotemporal EEG data. This advanced approach achieves high accuracy and low false prediction rates, aiding clinical diagnosis.

Keywords:

EEG GAT Seizure prediction Spatiotemporal attention Transformer

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Related Experiment Videos

Last Updated: Jul 18, 2025

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Epilepsy affects approximately 1% of the global population, necessitating effective seizure prediction methods.
Electroencephalogram (EEG) signals contain crucial spatiotemporal information reflecting neuronal interactions, posing a challenge for accurate analysis.
Exploiting both temporal dependencies within single-channel EEG and spatial correlations across multi-channel EEG is vital for seizure prediction.

Purpose of the Study:

To introduce Gatformer, a novel seizure prediction model.
To effectively utilize spatiotemporal information from EEG signals for improved seizure prediction.
To automatically identify significant brain region interactions for accurate seizure forecasting.

Main Methods:

Fusion of Graph Attention Network (GAT) and Transformer architectures.
Integration of temporal and spatial attention mechanisms to capture EEG spatiotemporal dynamics.
Analysis of temporal dependence in single-channel EEG and spatial correlations in multi-channel EEG.

Main Results:

Significant performance improvement over baseline models.
Achieved a low False Prediction Rate (FPR) of 0.0064/h on a private dataset.
Reported high average accuracy (98.25%), specificity (99.36%), and sensitivity (97.65%).

Conclusions:

The Gatformer model demonstrates state-of-the-art performance in seizure prediction.
Experiments confirm the model's robustness and generalization capabilities across different datasets.
High sensitivity and low FPR indicate significant potential for clinical assistance in epilepsy diagnosis and treatment.