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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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Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

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Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on

Jusciaane Chacon Vieira¹, Luiz Affonso Guedes¹, Mailson Ribeiro Santos¹

¹Department of Computer Engineering and Automation-DCA, Federal University of Rio Grande do Norte-UFRN, Natal 59078-900, RN, Brazil.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This study introduces a simplified method for detecting epileptic seizures using electroencephalogram (EEG) signals. The approach achieves over 95% accuracy with fewer features and channels, making mobile seizure detection feasible.

Keywords:

Explainable AI electroencephalography epilepsy machine learning

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

Published on: September 27, 2024

Area of Science:

Neurology
Biomedical Engineering
Artificial Intelligence

Background:

Epilepsy affects 50 million globally, causing seizures with diverse manifestations.
Seizures significantly impact quality of life, leading to social isolation and distress.
Current detection methods often rely on complex machine learning or deep learning on EEG signals.

Purpose of the Study:

To develop a simplified, explainable artificial intelligence (XAI) methodology for epileptic seizure detection.
To reduce the number of features and EEG channels required for accurate seizure detection.
To validate the effectiveness of simpler models for seizure detection without deep learning.

Main Methods:

Utilized Explainable Artificial Intelligence (XAI) for epileptic seizure detection.
Implemented a feature and channel reduction strategy for simpler classifiers.
Performed temporal domain analysis on EEG signals within a 1-second time window.

Main Results:

Achieved performance metrics exceeding 95% in accuracy, precision, recall, and F1-score.
Successfully detected epileptic seizures using only six features and five EEG channels.
Demonstrated robust generalization across a diverse patient cohort.

Conclusions:

Feature reduction in simpler models is adequate for effective epileptic seizure detection.
Strategic selection of electrodes and reduced attributes can support effective mobile seizure detection applications.
The proposed XAI methodology offers a promising, less complex alternative for seizure detection.