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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 19, 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

Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization

Morteza Behnam¹, Hossein Pourghassem¹

¹Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran.

Computer Methods and Programs in Biomedicine

|June 11, 2016

Summary

This study introduces a novel real-time algorithm for predicting epileptic seizures using electroencephalogram (EEG) signal analysis. The developed density-based approach achieves high accuracy in forecasting seizure events, aiding pediatric patient monitoring and drug therapy.

Keywords:

Bayesian classifier Hunting search algorithm Polynomial Newton interpolation algorithm Recursive least square filtering Seizure prediction

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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

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Last Updated: Mar 19, 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

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

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

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Epileptic seizure prediction is crucial for patient care and treatment.
Electroencephalogram (EEG) signal analysis is a key method for seizure detection.
Accurate time series estimation of EEG signals is vital for predicting seizure attacks.

Purpose of the Study:

To propose a novel density-based, real-time seizure prediction algorithm.
To leverage a trained offline seizure detection algorithm for online prediction.
To enhance the accuracy and timeliness of epileptic seizure forecasting.

Main Methods:

Offline: Preprocessing, histogram-based statistical features, Interpolated Histogram Feature (IHF), Seizure Distribution Model (SDM), hybrid optimization (Bayesian classifier and Hunting Search algorithm).
Online: Multi-Layer Perceptron (MLP) classifier trained with optimal features, enhanced Recursive Least Square (RLS) filter for sample-by-sample EEG estimation.
Density-based signal tracking scenario to update RLS filter parameters.

Main Results:

Evaluation on 104 hours of EEG data from 23 pediatric patients.
Achieved 86.56% accuracy and 86.53% precision using the trained MLP classifier.
Obtained a 97.27% recall rate for seizure prediction with a low false prediction rate (0.00215/hour).
Converged seizure prediction time to 6.64 seconds.

Conclusions:

The proposed real-time algorithm effectively predicts future EEG signal samples.
Density-based signal tracking improves the accuracy and timeliness of seizure detection.
The algorithm demonstrates acceptable performance using IHF and histogram-based features for seizure prediction.