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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: Apr 14, 2026

Author Spotlight: Assessing the Feasibility of Using Amplitude-Integrated EEG During Neonatal Transport

Author Spotlight: Assessing the Feasibility of Using Amplitude-Integrated EEG During Neonatal Transport

Published on: June 21, 2024

Clinical implementation of a neonatal seizure detection algorithm.

Andriy Temko¹, William Marnane¹, Geraldine Boylan²

¹Neonatal Brain Research Group, INFANT Research Centre, Dept. Electrical and Electronic Engineering, University College Cork, Cork, Ireland.

Decision Support Systems

|April 21, 2015

Summary

This summary is machine-generated.

Visualizing neonatal seizure detection output aids clinical decisions. A combination of binary and probabilistic traces is preferred by clinicians for improved accuracy and healthcare benefits.

Keywords:

Audification Clinical interface Decision making EEG Neonatal seizure detection Visualization

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Last Updated: Apr 14, 2026

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Published on: June 21, 2024

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

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Preterm EEG: A Multimodal Neurophysiological Protocol

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Published on: February 18, 2012

Area of Science:

Biomedical Engineering
Clinical Neurophysiology

Background:

Automated neonatal seizure detection systems are advancing towards clinical bedside implementation.
Effective visualization of seizure detection output is crucial for accurate interpretation and clinical utility.

Purpose of the Study:

To evaluate different visualization methods for neonatal seizure detection system output.
To analyze the impact of these visualizations on system performance in a clinical setting.
To gather clinical personnel feedback on the usefulness and accuracy of presented methods.

Main Methods:

Comparison of three visualization techniques: binary output, probabilistic trace, and spatio-temporal colormap.
Inclusion of audified neonatal electroencephalogram (EEG) as an alternative sensory output.
Conducting a survey among clinical staff to assess visualization method utility and accuracy.

Main Results:

The developed neonatal seizure detector, with appropriate visualization, can serve as a valuable clinical decision support tool.
Survey results indicate a preference for a combined binary output and probabilistic trace visualization.
Established a link between information visualization techniques and the computation of conventional performance metrics.

Conclusions:

A combined binary and probabilistic trace visualization is recommended for clinical implementation of neonatal seizure detection systems.
The decision support system shows promise for regulatory approval and clinical adoption.
The tool offers significant potential healthcare benefits for neonatal seizure management.