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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:

Antiepileptic Drugs: Modulators of Neurotransmitter Release Mediated by SV2A Protein

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Antiepileptic drugs, such as levetiracetam (Keppra) and brivaracetam (Briviact), have emerged as crucial tools in managing epilepsy. These medications exert their therapeutic effects by targeting the synaptic vesicle protein SV2A, a transmembrane glycoprotein primarily found in the brain.
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Epilepsy and Seizures: Overview

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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.
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A translational multimodal machine-learning prototype predicting valproate response in epilepsy treatment.

Simeon Platte¹, Afsheen Kumar¹, Giorgia Guerini²

¹Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Goethe University Frankfurt, University Hospital, Frankfurt, Germany.

|May 20, 2026

Summary

Developing a predictive model for valproic acid (VPA) response in epilepsy patients shows promise. Integrating genetic, cellular, and clinical data can improve seizure control and personalize antiseizure medication (ASM) selection.

Keywords:

antiseizure medications biomarker‐based machine learning classifier personalized treatment

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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:

Neurology
Pharmacogenomics
Biomarker Discovery

Background:

Epilepsy affects 1% of the global population, necessitating lifelong antiseizure medication (ASM) therapy.
Valproic acid (VPA) is a common first-line ASM, but only ~50% of patients achieve sustained seizure freedom.
Current ASM treatment selection is largely empirical, highlighting the need for personalized strategies.

Purpose of the Study:

To develop and validate a multimodal predictive model for estimating VPA response.
To support individualized treatment strategies for epilepsy patients.
To improve the selection of antiseizure medications.

Main Methods:

Cross-sectional treatment response modeling using data from the international Epi25 cohort and an independent Canadian cohort.
Integration of genetic variants (pharmacokinetic/pharmacodynamic genes), in vitro neuronal VPA response, and clinical features into a predictive algorithm.
Model performance assessed using accuracy, predictive values (NPV/PPV), and area under the curve (AUC).

Main Results:

The multimodal classifier achieved 63% balanced accuracy, 70% NPV, 60% PPV, and 0.73 AUC in the independent validation cohort.
Models using single or dual data modalities demonstrated lower predictive performance.
The findings suggest that integrating multiple data types enhances predictive power for VPA response.

Conclusions:

Integrating genetic, cellular, and clinical data can predict VPA treatment response with clinically meaningful accuracy.
This proof-of-concept study supports the feasibility of biomarker-informed ASM selection.
The approach may reduce the time to effective seizure control for epilepsy patients.