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Diagnosis and Prognosis: Prediction of Epilepsy using EEG Signals in Combination with Machine Learning Classifiers

by Alina Ahsan, Sifatullah Siddiqi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 15
Year of Publication: 2023
Authors: Alina Ahsan, Sifatullah Siddiqi
10.5120/ijca2023922841

Alina Ahsan, Sifatullah Siddiqi . Diagnosis and Prognosis: Prediction of Epilepsy using EEG Signals in Combination with Machine Learning Classifiers. International Journal of Computer Applications. 185, 15 ( Jun 2023), 30-37. DOI=10.5120/ijca2023922841

@article{ 10.5120/ijca2023922841,
author = { Alina Ahsan, Sifatullah Siddiqi },
title = { Diagnosis and Prognosis: Prediction of Epilepsy using EEG Signals in Combination with Machine Learning Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 15 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number15/32772-2023922841/ },
doi = { 10.5120/ijca2023922841 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:08.749143+05:30
%A Alina Ahsan
%A Sifatullah Siddiqi
%T Diagnosis and Prognosis: Prediction of Epilepsy using EEG Signals in Combination with Machine Learning Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 15
%P 30-37
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Epilepsy is a type of neurological disorder which impacts the brain’s central nervous system. While the effects vary from person to person, they com- monly include mental instability, moments of loss of awareness, and seizures.There are several classi- cal approaches for analysing EEG signals for seizures identification, all of which are time-consuming. Many seizure detection strategies based on machine learning techniques have recently been developed to replace traditional methods. A hybrid model for seizure prediction of 54-DWT mother wavelets analysis of EEG signals using GA (genetic algorithm) in combination with other five machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Net- work (ANN) Naive Bayes (NB) and Random Forest is used in this paper.Using these 5 ML classifiers, the efficacy of 14 possible combinations for two-class epileptic seizure detection is evaluated. Nonetheless, the ANN classifier beat the other classifiers in most dataset combinations and attained the highest accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Electroencephalogram (EEG) discrete wavelet transform (DWT) genetic algo- rithm (GA) support vector machine (SVM) artificial neural network (ANN) k-nearest neighbor (k-NN) naive bayes (NB) random forest (RF).

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