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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...

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Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A

Rabindra Gandhi Thangarajoo¹, Mamun Bin Ibne Reaz¹, Geetika Srivastava²

¹Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This summary is machine-generated.

Accurate detection of epileptic seizures using electroencephalogram (EEG) data is crucial. This review highlights wavelet and empirical mode decomposition techniques, finding Stockwell transform and Support Vector Machines (SVM) promising for efficient seizure detection.

Keywords:

electroencephalogram empirical mode decomposition random forest support vector machine wavelet

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Published on: January 29, 2018

Area of Science:

Biomedical Signal Processing
Machine Learning Applications
Neurological Disorder Analysis

Background:

Epileptic seizures affect over 50 million globally, with accurate detection aiding management.
Electroencephalogram (EEG) analysis is vital for understanding and detecting epileptic seizures.
Machine learning offers advanced tools for processing complex EEG data.

Purpose of the Study:

To review feature extraction techniques (wavelet and empirical mode decomposition) for epileptic seizure detection in EEG data.
To identify effective combinations of feature extraction and classification methods for EEG-based seizure detection.
To evaluate studies based on Journal Citation Report, feature selection, and classification methods.

Main Methods:

Systematic review of relevant studies on wavelet and empirical mode decomposition for EEG seizure detection.
Selection of articles based on Journal Citation Report, feature selection, and classifier usage.
Analysis of studies employing Random Forest and Support Vector Machine classifiers for high-dimensional EEG data.
Evaluation of performance metrics including sensitivity, specificity, and accuracy.

Main Results:

The review identified various feature extraction techniques and classifiers for EEG seizure detection.
Studies using Stockwell transform (a wavelet variant) as a feature extractor showed promising results.
Support Vector Machine (SVM) classifiers demonstrated effectiveness in handling high-dimensional EEG data.

Conclusions:

Feature extraction using Stockwell transform combined with SVM classifiers appears to be a highly effective approach for epileptic seizure detection.
The review underscores the importance of selecting appropriate feature extraction and classification methods for accurate and efficient seizure detection.
Further research validating these findings in diverse datasets is warranted.