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Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

Farah Mohammad*, Saad Al-Ahmadi

Department of Computer Science, King Saud University, Riyadh, 11543, Saudi Arabia

* Corresponding Author: Farah Mohammad. Email: email

Computers, Materials & Continua 2023, 74(1), 623-639. https://doi.org/10.32604/cmc.2023.032552

Abstract

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis. Their performance may sometime degrade due to missing information, selection of poor classification model and unavailability of quality data that are used to train the models for better prediction. This research work is an attempt to epileptic seizures detection by using a multi focus dataset based on EEG signals and brain MRI. The key steps of this work are: feature extraction having two different streams i.e., EEG using wavelet transformation along with SVD-Entropy, and MRI using convolutional neural network (CNN), after extracting features from both streams, feature fusion is applied to generate feature vector used by support vector machine (SVM) to diagnose the epileptic seizures. From the experimental evaluation and results comparison with the current state-of-the-art techniques, it has been concluded that the performance of the proposed scheme is better than the existing models.


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APA Style
Mohammad, F., Al-Ahmadi, S. (2023). Epileptic seizures diagnosis using amalgamated extremely focused EEG signals and brain MRI. Computers, Materials & Continua, 74(1), 623-639. https://doi.org/10.32604/cmc.2023.032552
Vancouver Style
Mohammad F, Al-Ahmadi S. Epileptic seizures diagnosis using amalgamated extremely focused EEG signals and brain MRI. Comput Mater Contin. 2023;74(1):623-639 https://doi.org/10.32604/cmc.2023.032552
IEEE Style
F. Mohammad and S. Al-Ahmadi, "Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI," Comput. Mater. Contin., vol. 74, no. 1, pp. 623-639. 2023. https://doi.org/10.32604/cmc.2023.032552



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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