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:

Epilepsy ll: Types

Epilepsy ll: Types

Recurrent seizures, stemming from abnormal electrical activity in the brain, are the defining characteristic of epilepsy, a chronic neurological condition. Because seizure features vary greatly, epilepsy is classified using two systems: by seizure type and by epilepsy syndromes. These classifications enable clinicians to describe seizure patterns and select suitable treatment strategies.I. Classification by Seizure Type1. Focal EpilepsyFocal epilepsy begins in one hemisphere of the brain.

You might also read

Related Articles

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

Sort by

Same author

Bayesian Image Analysis in Fourier Space.

Journal of the American Statistical Association·2026

Same author

Genome-sequencing of ocular fluid in a patient with syphilitic uveitis: Strain identification and antimicrobial resistance detection.

American journal of ophthalmology case reports·2026

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Search research articles

Related Experiment Video

Updated: May 14, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.

Susanne G Mueller¹, Karl Young, Miriam Hartig

¹Center for Imaging of Neurodegenerative Diseases, VA Medical Center, San Francisco, USA. susanne.mueller@ucsf.edu

|January 29, 2013

Summary

This summary is machine-generated.

A novel Bayesian network approach accurately differentiates individual epilepsy types using brain imaging, even when scans appear normal. This method analyzes gray and white matter abnormalities for improved epilepsy diagnosis.

More Related Videos

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Published on: September 20, 2024

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Related Experiment Videos

Last Updated: May 14, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

Published on: September 20, 2024

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Area of Science:

Neuroimaging
Epilepsy Research
Artificial Intelligence in Medicine

Background:

Quantitative neuroimaging reveals gray and white matter abnormalities in different epilepsy types.
Individualized patterns of these abnormalities and their diagnostic utility remain unclear.

Purpose of the Study:

To develop and validate a multi-modality imaging Bayesian network for classifying individual non-lesional epilepsy types.
To assess the diagnostic potential of combining gray matter volume and white matter integrity data.

Main Methods:

A two-level Bayesian network approach was applied to 4T MRI data (structural and DTI) from controls and epilepsy patients.
Gray matter (GM) and fractional anisotropy (FA) abnormality maps were generated for each subject.
Graphical-Model-based Morphometric Analysis (GAMMA) and a second-level Bayesian network classified epilepsy types.

Main Results:

The Bayesian network achieved high specificity (0.87 for TLE-MTS/TLE-no, 0.86 for FLE) and sensitivity (0.84 for TLE-MTS, 0.72 for TLE-no, 0.64 for FLE).
The method successfully distinguished between temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial-temporal sclerosis, and frontal lobe epilepsy (FLE).

Conclusions:

A two-level multi-modality Bayesian network effectively classifies epilepsy types using neuroimaging data.
This approach demonstrates diagnostic potential even with subtle or visually normal imaging findings.