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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: May 27, 2026

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

An algorithm for seizure onset detection using intracranial EEG.

Alaa Kharbouch¹, Ali Shoeb, John Guttag

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. aak@mit.edu

Epilepsy & Behavior : E&B

|November 15, 2011

Summary

This summary is machine-generated.

This study presents a machine learning algorithm for real-time seizure detection from intracranial EEG (iEEG). The patient-specific approach achieved 97% seizure detection with a 5-second delay and low false alarms.

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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

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Published on: June 23, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Intracranial EEG (iEEG) is crucial for epilepsy monitoring.
Seizure detection faces challenges due to patient heterogeneity and competing performance metrics (sensitivity, latency, false alarms).
Automated machine learning offers a solution for personalized seizure detection.

Purpose of the Study:

To develop and evaluate a machine learning algorithm for real-time, patient-specific seizure onset detection from iEEG.
To address the heterogeneity of seizure patterns across patients and within individuals.
To optimize sensitivity while minimizing detection latency and false alarm rates.

Main Methods:

Extraction of temporal and spectral features from all intracranial EEG channels.
Training a pattern recognition component using feature vectors.
Testing the algorithm on continuous, unseen iEEG data from individual patients.

Main Results:

The algorithm detected 97% of 67 seizures across 10 patients (875+ hours of data).
Median detection delay was 5 seconds, with a median false alarm rate of 0.6 per 24 hours.
100% sensitivity was achieved for 8 out of 10 patients.

Conclusions:

A sensitive, specific, and low-latency machine learning system for seizure detection from iEEG is feasible.
Patient-specific algorithms can overcome the heterogeneity of seizure patterns.
This approach holds promise for automated seizure detection in clinical practice.