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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...

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Updated: Apr 17, 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

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Paul Fergus¹, David Hignett¹, Abir Hussain¹

¹Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.

Biomed Research International

|February 25, 2015

Summary

Automated machine learning accurately detects seizure activity from electroencephalogram data. This approach improves upon existing methods, aiding earlier epilepsy diagnosis and treatment investigation.

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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

Area of Science:

Neurology
Artificial Intelligence
Medical Diagnostics

Background:

Epilepsy diagnosis is challenging, especially in early stages, often requiring neurologist expertise.
Electroencephalography (EEG) and magnetic resonance imaging (MRI) support epilepsy diagnosis but EEG interpretation is time-consuming and costly.
Automated detection of seizure activity from EEG could streamline diagnosis and treatment.

Purpose of the Study:

To present a supervised machine learning approach for classifying seizure and non-seizure EEG records.
To evaluate the performance of a k-class nearest neighbour classifier for automated seizure detection.

Main Methods:

A supervised machine learning model was developed to classify EEG records.
The model utilized an open dataset comprising 342 seizure and non-seizure records.
A k-class nearest neighbour classifier was employed for the classification task.

Main Results:

The machine learning approach demonstrated significant improvement over existing studies, up to 10% in many cases.
Achieved high performance metrics: 93% sensitivity, 94% specificity, and 98% area under the curve (AUC).
A global error rate of 6% was recorded, indicating robust classification accuracy.

Conclusions:

The proposed automated machine learning approach shows clinical potential for investigating suspected seizure disorders.
This method can aid in earlier and more efficient diagnosis of epilepsy.
Further applications in clinical settings for EEG analysis are proposed.