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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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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

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Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks.

Ling Guo¹, Daniel Rivero, Alejandro Pazos

¹Department of Information Technologies and Communications, University of La Coruña, A Coruña, Spain. lguo@udc.es

Journal of Neuroscience Methods

|September 7, 2010

Summary

This study introduces a new method for automatically detecting epileptic seizures using multiwavelet transform and artificial neural networks. The approach accurately classifies electroencephalogram (EEG) signals, improving epilepsy diagnosis.

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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

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Last Updated: Jun 9, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

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 characterized by recurrent seizures.
Electroencephalogram (EEG) is crucial for epilepsy diagnosis, but seizures are unpredictable.
Automatic seizure detection in EEG is essential for clinical applications.

Purpose of the Study:

To develop a novel method for automatic epileptic seizure detection in EEG signals.
To utilize multiwavelet transform and approximate entropy for feature extraction.
To combine these features with an artificial neural network for accurate classification.

Main Methods:

Applied multiwavelet transform to EEG signals for feature extraction.
Calculated approximate entropy to quantify signal complexity and irregularity.
Employed an artificial neural network for classifying EEG signals into seizure and non-seizure states.
Validated the method on a public EEG dataset.

Main Results:

The proposed method achieved high accuracy in detecting epileptic seizures.
The combination of multiwavelet features and approximate entropy proved effective.
The artificial neural network successfully classified EEG signals with high precision.
The method demonstrated success in two distinct classification tasks.

Conclusions:

The novel method offers a promising approach for automatic epileptic seizure detection.
Multiwavelet transform and approximate entropy are valuable tools for EEG analysis.
The developed system can aid in the clinical diagnosis and management of epilepsy.