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Diagnosis and Prognosis: Literature Review on Prediction of Epilepsy using Machine Learning Techniques

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/ijca2023922840

Alina Ahsan, Sifatullah Siddiqi . Diagnosis and Prognosis: Literature Review on Prediction of Epilepsy using Machine Learning Techniques. International Journal of Computer Applications. 185, 15 ( Jun 2023), 10-29. DOI=10.5120/ijca2023922840

@article{ 10.5120/ijca2023922840,
author = { Alina Ahsan, Sifatullah Siddiqi },
title = { Diagnosis and Prognosis: Literature Review on Prediction of Epilepsy using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 15 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 10-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number15/32771-2023922840/ },
doi = { 10.5120/ijca2023922840 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:08.038585+05:30
%A Alina Ahsan
%A Sifatullah Siddiqi
%T Diagnosis and Prognosis: Literature Review on Prediction of Epilepsy using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 15
%P 10-29
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Researchers are working to integrate machine learn- ing (ML) and artificial intelligence (AI) tools to im- prove and develop clinical practice. Machine learn- ing is becoming more important in medical image analysis. One of the fundamental goals of health- care is to provide timely preventative measures by early disease diagnosis and prognosis. This is cer- tainly relevant for epilepsy, which is characterized by recurring and unpredictable episodes. If epilep- tic seizures can be detected in advance, patients can avoid the unfavourable repercussions. Seizure prog- nosis remains an unsolved problem despite decades of research. This is likely to continue partly due to a lack of information to resolve this issue .Promis- ing new advancements in the ML-based techniques have the ability to alter the situation in the detec- tion and prediction of ES. We present a complete re- view of cutting-edge ML techniques for early seizure prediction with the help of EEG signals. We will highlight research gaps and problems and give rec- ommendations for future initiatives.

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Index Terms

Computer Science
Information Sciences

Keywords

EEG Machine Learning Seizure

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