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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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Proceedings of the 15<sup>th</sup> International Newborn Brain Conference: Neonatal Neurocritical Care, seizures, and continuous aEEG and /or EEG monitoring: Fota Island, Cork, Ireland, February 28<sup>th</sup> - March 2<sup>nd</sup> 2024.

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Related Experiment Video

Updated: May 14, 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

Dynamic time warping based neonatal seizure detection system.

Rehan Ahmed¹, Andrey Temko, William Marnane

¹Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland. rehan.andreyt@eleceng.ucc.ie

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This summary is machine-generated.

This study enhances neonatal seizure detection by incorporating temporal evolution patterns. A modified support vector machine (SVM) with a Gaussian dynamic time warping kernel improves seizure classification accuracy.

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Last Updated: May 14, 2026

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Published on: March 27, 2021

Area of Science:

Biomedical Engineering
Neuroscience
Signal Processing

Background:

Neonatal seizures exhibit complex temporal evolution in frequency, morphology, and propagation.
Accurate detection of neonatal seizures is crucial for timely intervention and improved outcomes.
Existing seizure detection methods may not fully capture the dynamic nature of seizure patterns.

Purpose of the Study:

To develop an improved neonatal seizure detection system by integrating the temporal evolution characteristics of seizures.
To modify a previously developed support vector machine (SVM)-based detector to handle variable-length seizure sequences.
To evaluate the performance of the modified detector against conventional methods.

Main Methods:

Modification of a support vector machine (SVM) neonatal seizure detector.
Substitution of the Gaussian kernel with a Gaussian dynamic time warping (DTW) kernel.
Classification of variable-length sequences of feature vectors representing neonatal seizures.

Main Results:

The modified SVM detector demonstrated favorable preliminary results compared to the conventional SVM.
The Gaussian dynamic time warping kernel enabled the classification of variable-length seizure sequences.
The approach shows potential for enhancing neonatal seizure detection capabilities.

Conclusions:

Incorporating temporal evolution characteristics significantly improves neonatal seizure detection.
The modified SVM with a Gaussian DTW kernel offers a promising approach for analyzing dynamic seizure patterns.
Fusion of these approaches is expected to advance the state-of-the-art in neonatal seizure detection systems.