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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: Apr 3, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

Three-Phase Seizure Segmentation in Stereotactic EEG Using Envelope-Based Multivariate Changepoint Analysis.

Himanshu Kumar¹, N P Guhan Seshadri¹, David Martinez¹

¹Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, 44195, USA.

Annals of Biomedical Engineering

|April 2, 2026

Summary

This summary is machine-generated.

This study introduces a new framework for precisely identifying seizure phases in intracranial EEG recordings. The method accurately delineates seizure onset, transition, and termination, aiding epilepsy research and presurgical evaluations.

Keywords:

Changepoint detection Epilepsy Multivariate time series Stereotactic EEG (SEEG)

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

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:

Neuroscience
Computational Biology
Medical Technology

Background:

Accurate seizure phase segmentation in intracranial EEG (iEEG) is critical for understanding epilepsy dynamics.
Precise characterization supports presurgical evaluation in drug-resistant focal epilepsy.

Purpose of the Study:

To evaluate a semi-supervised changepoint detection framework for reliably delineating seizure phases.
Investigate the framework's ability to segment ictal onset, intra-ictal transition, and seizure termination.

Main Methods:

A three-phase segmentation pipeline integrating multivariate envelope-based features (amplitude, bandpower, line length, spectral entropy).
Utilized the Pruned Exact Linear Time algorithm with optimized window lengths and phase-specific weights via cross-validation.
Employed data augmentation by extending analysis windows with pre- and post-ictal data to ensure length invariance.

Main Results:

Mean absolute errors were 4.19s (onset), 6.93s (transition), and 3.82s (termination).
Detection accuracies within ±5s were 71.6% (onset), 60.0% (transition), and 75.0% (termination).
Phase-specific feature importance revealed distinct contributions of amplitude, spectral, and complexity measures throughout seizure phases.

Conclusions:

The framework offers temporal precision comparable to inter-rater reliability in iEEG seizure phase segmentation.
Provides an interpretable, data-driven method for comprehensive seizure phase characterization.
Demonstrates potential utility in clinical decision-making for epilepsy management.