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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 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...

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Related Experiment Video

Updated: May 6, 2026

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models.

Uriel Fennig^1,2, Nadav Amir¹, Maya Schiller¹

¹Department of Neurology, The Sheba Medical Center at Tel Hashomer, Ramat Gan, Israel.

Annals of Clinical and Translational Neurology

|May 5, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in identifying epilepsy surgery candidates from clinical notes, improving access to care. This AI-driven approach can help detect eligible patients missed in routine practice.

Keywords:

decision support epilepsy surgery large language models screening

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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

Published on: June 13, 2016

Area of Science:

Artificial Intelligence in Medicine
Neurology
Clinical Decision Support

Background:

Epilepsy surgery is underutilized, with many eligible patients not referred.
Identifying suitable candidates from unstructured clinical notes is challenging.

Purpose of the Study:

To evaluate large language models (LLMs) as decision-support tools for screening epilepsy surgery candidates.
To assess LLMs' ability to stratify patients based on prognostic indicators.

Main Methods:

Retrospective analysis of 110 Hebrew-language clinical notes from a tertiary epilepsy clinic.
Six LLMs (Gemini, GPT, o4-mini) were prompted to extract eligibility criteria and prognostic parameters.
Model outputs were compared against expert manual review.

Main Results:

LLMs achieved high sensitivity (up to 1.00) and specificity (up to 0.96) in identifying surgical eligibility.
45% of eligible patients had no prior surgical consideration.
Models accurately evaluated prognostic scores (Seizure Freedom Scale) and presurgical evaluations.

Conclusions:

LLMs can serve as effective decision-support tools to identify overlooked epilepsy surgery candidates.
This AI approach offers a practical and scalable screening method for diverse clinical settings.
High performance was demonstrated even with raw, non-English clinical data.