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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 7, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during

Supachan Traitruengsakul¹, Laurie E Seltzer², Alex R Paciorkowski^2,3

¹Biomedical Engineering Department, Rochester Institute of Technology, Rochester, NY, USA.

Medical & Biological Engineering & Computing

|February 11, 2017

Summary

A new algorithm accurately detects infantile spasms (ISS) by analyzing electroencephalogram (EEG) patterns. This quantitative assessment improves diagnosis and management of this severe epilepsy syndrome in infants.

Keywords:

Classification Feature extraction Hypsarrythmia Nonnegative matrix factorization Time–frequency representations

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Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

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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

Area of Science:

Neurology
Medical Technology
Signal Processing

Background:

Infantile spasms (ISS) is a severe epilepsy syndrome affecting infants under one year old.
Diagnosis relies on seizure semiology and electroencephalogram (EEG) hypsarrhythmia (HYPS), but interpretation can be subjective.
Existing diagnostic tools and algorithms lack accuracy for ISS detection.

Purpose of the Study:

To develop a novel algorithm for quantitative assessment of ISS in hypsarrhythmia (HYPS) EEG.
To accurately localize epileptic discharges associated with ISS.

Main Methods:

Extraction of novel time-frequency features from EEG signals.
Localization of epileptic discharges using a support vector machine classifier.
Evaluation on an EEG dataset of infants with ISS.

Main Results:

The algorithm achieved a 98% true positive rate and a 7% false negative rate.
Demonstrated significant improvement over clinically available software.
Provided a quantitative assessment of ISS in HYPS.

Conclusions:

The developed automated method enhances the quantitative assessment of ISS in HYPS.
This tool has the potential to significantly improve therapy management for infantile spasms.
Objective EEG analysis can overcome subjective interpretation challenges in ISS diagnosis.