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Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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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Updated: Feb 16, 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

Epileptic seizure detection in EEG signal using machine learning techniques.

Abeg Kumar Jaiswal¹, Haider Banka²

¹Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India. abegiitdhanbad@gmail.com.

Australasian Physical & Engineering Sciences in Medicine

|December 22, 2017

Summary

This summary is machine-generated.

This study introduces novel machine learning methods, subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA), for faster automated epilepsy detection in EEG signals. These techniques improve seizure classification accuracy compared to traditional methods.

Keywords:

Classification Cross-subpattern correlation-based PCA (SubXPCA)Electroencephalogram (EEG) signal Feature extraction Principal component analysis (PCA)Subpattern based PCA (SpPCA)Support Vector Machine (SVM)

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

Area of Science:

Neurology
Biomedical Engineering
Machine Learning

Background:

Epilepsy is a neurological disorder causing seizures, detectable via electroencephalograms (EEGs).
Traditional EEG analysis for seizure detection is time-intensive.
Machine learning offers automated solutions for EEG analysis.

Purpose of the Study:

To propose and evaluate two novel feature extraction techniques, SpPCA and SubXPCA, for automated seizure detection in EEG signals.
To compare the performance of these new methods with existing techniques.

Main Methods:

Feature extraction using subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA).
Classification of EEG signals into seizure and non-seizure categories using Support Vector Machine (SVM) with a radial basis kernel.
Experiments conducted on a benchmark EEG dataset (500 signals) with tenfold cross-validation.

Main Results:

The proposed SpPCA and SubXPCA methods demonstrated effectiveness in exploring EEG subpattern correlations for improved decision-making.
Classification accuracy was evaluated across seven experimental cases.
Performance was benchmarked against existing automated seizure detection techniques.

Conclusions:

The developed SpPCA and SubXPCA approaches show promise for efficient and accurate automated seizure detection in EEG.
These machine learning techniques offer a viable alternative to time-consuming traditional EEG analysis methods.