Journal



Frequency: Quarterly

ISSN (Online) : 2788-7669

ISSN (Print) : 2789-1801




Journal of Machine and Computing

Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction



Journal of Machine and Computing

Received On : 10 May 2024

Revised On : 28 July 2024

Accepted On : 04 August 2024

Published On : 05 October 2024

Volume 04, Issue 04

Pages : 1069-1078



DOI
https://doi.org/10.53759/7669/jmc202404099
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Abstract

Epilepsy is a neurological condition that is found in most people all over the world, and the ability to accurately anticipate seizures in epileptic patients has a significant impact on both their level of protection and their overall quality of life. This research proposes a unique patient specific seizure prediction approach based on Deep Learning (DL) using long-term scalp electroencephalogram (EEG) recordings to predict seizure onset. Preictal brain states should be adequately detected and differentiated from the prevalent interictal brain states as early as possible to make this technology acceptable for real-time use. A single automated system has been designed for the Features Extraction (FE) and classification processes. The raw EEG signal that has not been pre-processed is considered the input to the system, and the signal is further reduced using subsequent computations. An innovative reconstruction approach using Variational Auto-Encoder Generative Adversarial Networks (VAE+C+GAN) with the Cramer Distance (CD) and a Temporal-Spatial-Frequency (TSF) loss function is presented in this research work. The machine that discriminates receives instructions to differentiate between created tests and actual samples, while the generator is verified to produce false samples that the discriminator does not recognize as fake. The proposed VAE+C+GAN’s experimental results have been examined, and a classification accuracy of 95% has been achieved. According to the experiment's findings, the VAE-C-GAN performs better than the current EEG classification system and has excellent potential for real-time applications.


Keywords

Seizure, Cramer Distance, Variational Autoencoder, Electroencephalogram, Epilepsy Diagnosis and Detection.


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Acknowledgements

We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.


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No funding was received to assist with the preparation of this manuscript.


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The authors would like to thank to the reviewers for nice comments on the manuscript.


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Cite this article

Hayder M A Ghanimi, Santhi Sri T, Vijaya Bhaskar Sadu, Pachipala Yellamma, Surya U and Kamal Poon, “Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction”, Journal of Machine and Computing, pp. 1069-1078, October 2024. doi:10.53759/7669/jmc202404099.


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© 2024 Hayder M A Ghanimi, Santhi Sri T, Vijaya Bhaskar Sadu, Pachipala Yellamma, Surya U and Kamal Poon. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


                            

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