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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: Mar 13, 2026

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

Detecting Neonatal Seizures With Computer Algorithms.

Andriy Temko¹, Gordon Lightbody

¹INFANT Research Centre, University College Cork, Cork, Ireland.

Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society

|October 18, 2016

Summary

This summary is machine-generated.

Automated seizure detection using electroencephalogram (EEG) is crucial for neonatal intensive care units. This review examines algorithms for reliable, robust seizure detection to aid clinical decisions when neurophysiologists are unavailable.

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Last Updated: Mar 13, 2026

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

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Published on: September 6, 2017

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Published on: June 11, 2020

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Published on: March 27, 2021

Area of Science:

Neonatal neurology
Biomedical engineering
Computational neuroscience

Background:

Electroencephalogram (EEG) is the gold standard for detecting newborn seizures.
Prolonged EEG monitoring is standard in neonatal intensive care units (NICUs).
Limited availability of neurophysiologists necessitates automated seizure detection solutions.

Purpose of the Study:

To review existing algorithms for automatic neonatal seizure detection.
To analyze methods for exploiting seizure signal information.
To discuss challenges and progress in clinical decision support tools for neonatal seizures.

Main Methods:

Review of algorithms based on seizure signal signature extraction.
Analysis of classification methods using rules, thresholds, and data-driven approaches.
Examination of techniques incorporating spatiotemporal seizure context.

Main Results:

Common methods include mimicking neurophysiologists and using mathematical models.
Classification relies on heuristic or data-derived rules and thresholds.
Spatiotemporal context analysis is an emerging area.

Conclusions:

A need exists for reliable, robust automated seizure detection algorithms.
Current tools face challenges in design, validation, and clinical integration.
Further development is required to address outstanding challenges in neonatal seizure detection.