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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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Updated: Feb 20, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI.

John Del Gaizo¹, Neda Mofrad¹, Jens H Jensen²

¹Department of Neurology Medical University of South Carolina Charleston SC USA.

Brain and Behavior

|October 28, 2017

Summary

This summary is machine-generated.

Diffusion kurtosis imaging (DKI) shows promise in classifying temporal lobe epilepsy (TLE) by identifying extrahippocampal damage. Mean kurtosis (MK) from DKI achieved higher accuracy than traditional diffusion tensor imaging (DTI) metrics in predicting TLE.

Keywords:

Magnetic Resonance Imaging (MRI)diffusion kurtosis imaging epilepsy machine learning support vector machines

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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

Area of Science:

Neuroimaging
Epilepsy Research
Medical Diagnostics

Background:

Medial temporal lobe epilepsy (TLE) often involves extrahippocampal damage.
The consistency of microstructural abnormalities outside the hippocampus for TLE classification using diffusion MRI is uncertain.

Purpose of the Study:

To evaluate the efficacy of diffusion MRI metrics, specifically mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA), in classifying TLE.
To implement a support vector machine (SVM) model for TLE prediction using these diffusion MRI metrics.

Main Methods:

Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) data were acquired from 32 TLE patients and 36 healthy controls.
An ensemble of SVM models with Bayesian-optimized regularization was trained and evaluated using fivefold cross-validation repeated 1000 times.

Main Results:

Mean kurtosis (MK) demonstrated superior accuracy (0.82) compared to fractional anisotropy (FA) (0.68) and mean diffusivity (MD) (0.51) in predicting TLE.
The most predictive MK voxels were located in the inferior medial temporal lobes, areas characterized by complex fiber anatomy.

Conclusions:

DKI, particularly MK, is more effective than DTI (MD, FA) in detecting TLE-associated pathological features.
Identifying consistent microstructural abnormalities could lead to TLE phenotyping and personalized treatment strategies.