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

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Comparative analysis of classifiers for developing an adaptive computer-assisted EEG analysis system for diagnosing

Malik Anas Ahmad¹, Yasar Ayaz¹, Mohsin Jamil¹

¹SMME, National University of Sciences & Technology, Islamabad 44000, Pakistan.

Biomed Research International

|April 3, 2015

Summary

This study introduces a self-improving computer-assisted system for diagnosing epilepsy using electroencephalogram (EEG) analysis. The system learns from incorrect classifications, enhancing diagnostic accuracy for neurologists.

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Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Medical Informatics
Computational Neuroscience
Machine Learning

Background:

Computer-assisted analysis of electroencephalogram (EEG) aids epilepsy diagnosis.
Current systems require mechanisms for self-improvement based on clinical feedback.

Purpose of the Study:

To develop a self-improving computer-assisted EEG analysis system for epilepsy diagnosis.
To enable clinicians to correct false classifications and enhance system performance.
To compare the efficacy of different machine learning classifiers within the proposed system.

Main Methods:

Discrete Wavelet Transform (DWT) applied to EEG signal epochs.
Dimensionality reduction using Principal Component Analysis (PCA).
Classification using Support Vector Machine (SVM), Quadratic Discriminant Analysis (QDA), and Artificial Neural Networks (ANN).

Main Results:

Support Vector Machine (SVM) demonstrated the highest classification performance.
The developed mechanism allows for user adaptation and system self-improvement.
Individual channel processing was employed for enhanced analysis.

Conclusions:

The proposed system is viable for self-improving, user-adapting computer-assisted epilepsy diagnosis.
SVM is the preferred classifier for this specific EEG analysis task.
This approach enhances the utility of computer-assisted EEG analysis in clinical practice.