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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:

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Related Experiment Video

Updated: Jul 22, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI.

Md Navid Akbar¹, Sebastian Ruf¹, Marianna La Rocca²

¹Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA 02115, USA.

Computational Diffusion MRI : MICCAI Workshop

|July 25, 2023

Summary

This summary is machine-generated.

Identifying traumatic brain injury (TBI) patients at risk for late seizures is crucial. Diffusion-weighted MRI with lesion normalization shows promise for predicting post-traumatic epilepsy (PTE) risk using white matter tract biomarkers.

Keywords:

Biomarker Classification Diffusion MRI Feature selection Lesion normalization Post-traumatic epilepsy

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Published on: April 14, 2014

Area of Science:

Neuroscience
Medical Imaging
Neurology

Background:

Traumatic brain injury (TBI) can lead to lifelong disabilities, including seizures and post-traumatic epilepsy (PTE).
Predicting seizure risk in TBI patients is challenging.
Standard neuroimaging analysis is complicated by physical deformations in moderate-to-severe TBI.

Purpose of the Study:

To identify TBI patients at risk of developing late seizures.
To evaluate diffusion-weighted MRI (dMRI) preprocessing strategies for biomarker detection.
To investigate the utility of fractional anisotropy (FA) features from white matter tracts.

Main Methods:

Applied four dMRI preprocessing strategies to TBI patient data.
Included a novel lesion normalization technique for dMRI data.
Utilized fractional anisotropy (FA) features for seizure prediction.

Main Results:

The preprocessing pipeline incorporating lesion normalization yielded the best prediction performance.
Achieved a mean accuracy of 0.819 and a mean area under the curve of 0.785.
Identified specific white matter tract alterations as potential biomarkers.

Conclusions:

dMRI with lesion normalization is a promising approach for predicting late seizures after TBI.
Specific white matter tract alterations can serve as biomarkers for post-traumatic epilepsy risk.
This method aids in identifying high-risk TBI patients for closer monitoring and intervention.