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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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Related Experiment Video

Updated: Sep 18, 2025

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

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Published on: June 23, 2023

Machine and deep learning methods for epileptic seizure recognition using EEG data: A systematic review.

Raja Mourad¹, Ahmad Diab², Zaher Merhi²

¹Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France; Signal Processing, Computer Hardware, Signals and Control Systems, Lebanese International University LIU, Tripoli, Lebanon; Signal Processing, Computer Hardware, Signals and Control Systems, International University of Beirut BIU, Beirut, Lebanon.

|June 25, 2025

Summary

This summary is machine-generated.

Machine learning and deep learning offer automated solutions for detecting, classifying, and predicting epileptic seizures (ES) from EEG data. This review synthesizes recent advancements to improve the reliability and clinical application of these AI-driven seizure recognition systems.

Keywords:

Deep Learning EEG Epilepsy Feature Extraction Machine Learning Seizure Classification Seizure Detection Seizure Prediction

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Published on: September 27, 2024

Area of Science:

Neurology
Artificial Intelligence
Biomedical Signal Processing

Background:

Epilepsy affects millions globally, with diagnosis relying on Electroencephalography (EEG).
Manual EEG analysis for seizure detection is complex and time-consuming.
Machine Learning (ML) and Deep Learning (DL) show promise for automated seizure recognition.

Purpose of the Study:

To systematically review ML and DL approaches for EEG-based Epileptic Seizure (ES) recognition.
To provide a comprehensive overview of detection, classification, and prediction tasks.
To identify challenges and emerging trends in AI for seizure recognition.

Main Methods:

Systematic review of peer-reviewed studies from 2013-2023.
Analysis of ML and DL methodologies applied to EEG data.
Evaluation of performance, strengths, and limitations of various models.

Main Results:

ML and DL techniques are effective for automated ES detection, classification, and prediction.
Key challenges include feature extraction, dataset selection, and model generalization.
Emerging trends include explainable AI, transfer learning, and federated learning.

Conclusions:

AI-driven methods can enhance the reliability and efficiency of seizure recognition systems.
Bridging the gap between AI methodologies and clinical applications is crucial.
Development of robust and interpretable ES detection frameworks is ongoing.