Title: Prediction of epileptic seizure using deep learning architectures

Authors: Vajravelu Ashok; J. Anitha; Isabel De la Torre Díez; D. Jude Hemanth

Addresses: Department of Electronics, Faculty of Electrical Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia ' Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India ' Department of Signal Theory and Communications, University of Valladolid, Spain ' Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India

Abstract: Neurological illnesses such as epilepsy are among the most frequent. Epileptic sufferers' lives are greatly impacted by early warnings of impending seizures. Using electroencephalogram signals, this research aims to create an epileptic seizure prediction algorithm that can automatically identify an epileptic seizure. Early seizure prediction using EEG data is now possible thanks to the latest machine learning algorithms. An average AUC of 0.74 is achieved by the new technique, compared to 0.72 for the state-of-the-art approach, a 3.25-fold increase in computing time. In-depth knowledge of seizure detection, classification, and potential future research areas may be gained through this presentation's cutting-edge methodologies and concepts. Predicting seizure activity might benefit from a modified atom search optimisation-based deep recurrent neural network. Numerous hidden layers are used by the deep recurrent neural network (DRNN) classifier to predict seizure activity.

Keywords: electroencephalogram; EEG; signal processing; epilepsy; CHBMIT dataset.

DOI: 10.1504/IJMEI.2025.145853

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.3, pp.267 - 278

Received: 23 Apr 2022
Accepted: 22 Jul 2022
Published online: 30 Apr 2025 *

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