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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.
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Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

Seizures l: Introduction

Seizures l: Introduction

Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...

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High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

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Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine.

Yuedong Song¹, Jon Crowcroft, Jiaxiang Zhang

¹Computer Laboratory, University of Cambridge, Cambridge, United Kingdom. ys340@cam.ac.uk

Journal of Neuroscience Methods

|July 25, 2012

Summary

A new method uses optimized sample entropy (O-SampEn) and extreme learning machine (ELM) for fast and accurate automatic epileptic seizure detection from electroencephalogram (EEG) data. This approach shows significant potential for real-time epilepsy monitoring.

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High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

Neurology
Biomedical Engineering
Signal Processing

Background:

Epilepsy is a common neurological disorder affecting 1% of the global population.
Electroencephalogram (EEG) is crucial for epilepsy diagnosis and management.
Manual analysis of extensive EEG data for epilepsy detection is impractical.

Purpose of the Study:

To develop a novel, automated method for epileptic seizure detection using EEG signals.
To address the limitations of manual EEG analysis in identifying epileptic patterns.

Main Methods:

Proposed an optimized sample entropy (O-SampEn) algorithm.
Combined O-SampEn with extreme learning machine (ELM) for signal classification.
Utilized a public dataset for method evaluation.

Main Results:

Achieved high detection accuracy for epileptic seizures.
Demonstrated very fast computation speed.
The method shows potential for real-time seizure detection applications.

Conclusions:

The proposed O-SampEn and ELM combination offers an effective solution for automated epileptic seizure detection.
The method's speed and accuracy are suitable for real-time monitoring.
This novel approach has significant implications for epilepsy management.