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Epileptic Seizures Detection Using iEEG Signals and Deep Learning Models

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Abstract

Epilepsy is a common neurological disorder that affects millions of people worldwide, and many patients do not respond well to traditional anti-epileptic drugs. To improve the lives of these patients, there is a need to develop accurate methods for predicting epileptic seizures. Seizure prediction involves classifying preictal and interictal states, which is a challenging classification problem. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great promise in analyzing and classifying EEG signals related to epilepsy. In this study, we proposed four deep learning models (S-CNN, Modif-CNN, CNN-SVM, and Comb-2CNN) to classify epilepsy states, which we evaluated on an iEEG dataset from the American Epilepsy Society database. Our models achieved high accuracy rates, with the S-CNN and Comb-2CNN models achieving 96.53%, CNN-SVM achieving 96.99%, and the Modif-CNN model achieving 97.96% in our experiments. These findings suggest that deep learning models could be an effective approach for classifying epilepsy states and could potentially improve seizure prediction methods, ultimately enhancing the quality of life for people with epilepsy.

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Data Availability

The dataset used in this study is public and can be found at the following links: https://kaggle.com/c/seizure-prediction, accessed on April 02, 2022.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/166/44.

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The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’, National Engineering School of Sfax, Sfax University, Sfax, Tunisia

    Nourane Abderrahim, Amira Echtioui, Rafik Khemakhem & Mohamed Ghorbel

  2. Higher Institute of Management of Gabès, Gabès University, Gabès, Tunisia

    Rafik Khemakhem

  3. King Abdulaziz University (KAU), Jeddah, Kingdom of Saudi Arabia

    Wassim Zouch

  4. Department IS, College of Computer Science, King Khaled University ‘KKU’, Abha, Kingdom of Saudi Arabia

    Ahmed Ben Hamida

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  1. Nourane Abderrahim
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  2. Amira Echtioui
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  3. Rafik Khemakhem
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  4. Wassim Zouch
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  5. Mohamed Ghorbel
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  6. Ahmed Ben Hamida
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Contributions

Conceptualization was done by NA and AE. All the literature reading and data gathering were performed by NA and AE. All the experiments and coding were performed by NA and AE. The formal analysis was performed by NA. Manuscript writing original draft preparation was done by NA. Review and editing was done by AE, RK, WZ, MG, and ABH. Visualization work was carried out by NA, AE, RK, WZ, MG, and ABH.

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Correspondence to Amira Echtioui.

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Abderrahim, N., Echtioui, A., Khemakhem, R. et al. Epileptic Seizures Detection Using iEEG Signals and Deep Learning Models. Circuits Syst Signal Process 43, 1597–1626 (2024). https://doi.org/10.1007/s00034-023-02527-8

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