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Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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:

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Related Experiment Video

Updated: Jun 23, 2026

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

Deep learning enabled decision support systems in epilepsy surgery: a scoping review.

Kai Yu¹, Shuang Zhou¹, Meijia Song¹

¹Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.

Npj Health Systems

|June 22, 2026

Summary

Deep learning shows promise for epilepsy surgery decision support, but current evidence is limited. More multi-center data and rigorous evaluation are needed for safe, scalable adoption in clinical workflows.

Keywords:

Computational biology and bioinformatics Health care Mathematics and computing Medical research Scientific community

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$Stereo-Electro-Encephalo-Graphy (SEEG) With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note$

Stereo-Electro-Encephalo-Graphy (SEEG) With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

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Operative Technique and Nuances for the Stereoelectroencephalographic (SEEG) Methodology Utilizing a Robotic Stereotactic Guidance System

Operative Technique and Nuances for the Stereoelectroencephalographic (SEEG) Methodology Utilizing a Robotic Stereotactic Guidance System

Published on: June 9, 2023

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Last Updated: Jun 23, 2026

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

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Published on: May 20, 2016

$Stereo-Electro-Encephalo-Graphy (SEEG) With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note$

Stereo-Electro-Encephalo-Graphy (SEEG) With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

Published on: June 13, 2016

Operative Technique and Nuances for the Stereoelectroencephalographic (SEEG) Methodology Utilizing a Robotic Stereotactic Guidance System

Operative Technique and Nuances for the Stereoelectroencephalographic (SEEG) Methodology Utilizing a Robotic Stereotactic Guidance System

Published on: June 9, 2023

Area of Science:

Neurology
Artificial Intelligence
Medical Informatics

Background:

Deep learning (DL) is gaining traction for epilepsy surgery decision support.
Evidence for DL implementation across the entire epilepsy surgery pathway is scarce.

Purpose of the Study:

To map DL decision support systems in epilepsy surgery.
To characterize datasets, modeling, validation, and workflow integration.
To identify gaps for future research and clinical implementation.

Main Methods:

Scoping review of 145 studies (Jan 2018 - May 2025).
Analysis of DL systems across surgical stages and clinical tasks.
Characterization of datasets, modeling techniques, validation, and workflow integration.

Main Results:

Literature focuses on pre-operative applications; intra-operative and post-operative studies are lacking.
Most studies use small, single-center, non-public datasets with supervised CNN models.
External validation and workflow integration are uncommon, limiting generalizability and readiness.

Conclusions:

Significant gaps exist in generalizability, workflow readiness, and equity.
Priorities include multi-center data, rigorous cross-site evaluation, and clinically meaningful endpoints.
These are crucial for safe and scalable adoption of DL in epilepsy surgery.