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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...

Epilepsy ll: Types

Epilepsy ll: Types

Recurrent seizures, stemming from abnormal electrical activity in the brain, are the defining characteristic of epilepsy, a chronic neurological condition. Because seizure features vary greatly, epilepsy is classified using two systems: by seizure type and by epilepsy syndromes. These classifications enable clinicians to describe seizure patterns and select suitable treatment strategies.I. Classification by Seizure Type1. Focal EpilepsyFocal epilepsy begins in one hemisphere of the brain.

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Related Experiment Video

Updated: May 13, 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

An explainable EEG epilepsy detection model using friend pattern.

Turker Tuncer¹, Sengul Dogan²

¹Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, 23119, Turkey.

Scientific Reports

|May 15, 2025

Summary

This summary is machine-generated.

This study introduces a novel explainable feature engineering (XFE) model for epilepsy detection using electroencephalography (EEG) signals. The FriendPat feature extraction function achieved high accuracy in classifying EEG data for epilepsy diagnosis.

Keywords:

Connectome theory Directed lobish EEG signal classification Epilepsy FriendPat XFE

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Last Updated: May 13, 2026

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Published on: June 6, 2015

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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

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Electroencephalography (EEG) signals are crucial for brain activity analysis and cost-effective for data acquisition.
EEG is widely utilized for epilepsy detection, necessitating advanced classification methods.
Existing methods may lack interpretability, hindering clinical adoption.

Purpose of the Study:

To demonstrate the epilepsy detection capability of a new relation-centric feature extraction function.
To introduce a novel explainable feature engineering (XFE) model for EEG signal classification.
To evaluate the performance of the proposed model on a public epilepsy dataset.

Main Methods:

Introduced Friend Pattern (FriendPat), a distance- and voting-based feature extraction function.
Employed a cumulative and iterative feature selector for optimal feature selection.
Utilized a t algorithm-based k-nearest neighbors (tkNN) classifier for classification.
Generated Directed Lobish's (DLob) symbols and strings for artifact classification and explainable results.

Main Results:

The FriendPat XFE model achieved 99.61% accuracy with 10-fold cross-validation (CV).
The model attained 79.92% accuracy using leave-one-subject-out (LOSO) CV.
The XFE model generates a connectome diagram, enhancing the interpretability of epilepsy detection.

Conclusions:

The presented FriendPat XFE model demonstrates high efficacy in EEG-based epilepsy detection.
The explainable nature of the model, including connectome diagram generation, aids in understanding diagnostic features.
The model shows significant potential for improving automated epilepsy diagnosis systems.