Document Type

Article

Source of Publication

Tem Journal-Technology Education Management Informatics

Publication Date

5-25-2026

Abstract

Epilepsy is a persistent neurological disorder that affects over 50 million people worldwide, with nearly one-third of patients remaining unresponsive to conventional therapeutic treatments. This study introduces a progressively adaptive seizure prediction framework designed to enhance early detection and clinical decision-making. The proposed model employs a deep learning strategy grounded in continual learning (CL) principles, using Convolutional Neural Networks (CNNs) in combination with knowledge distillation techniques. This enables the model to assimilate new data while retaining previously learned information. The approach was evaluated on the publicly available Bonn University EEG dataset, following a sequential learning process in which each successive model iteration improved prediction performance. The final model version (Model C) demonstrated significantly improved predictive performance, showing strong stability and generalization across sequential learning stages. Its results clearly outperformed conventional machine learning classifiers, including support vector machines, logistic regression, and random forests. The results demonstrate that continual learning architectures can effectively manage evolving EEG data streams, offering stable and accurate seizure prediction without retraining from scratch. Overall, this research emphasizes the benefits of continual deep learning in clinical applications, establishing a foundation for intelligent, scalable, and real-time seizure prediction systems for AI-assisted healthcare monitoring.

ISSN

2217-8309

Publisher

Association for Information Communication Technology Education and Science (UIKTEN)

Volume

15

Issue

2

First Page

978

Last Page

990

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

CNN, knowledge transfer, elastic weight consolidation, FIM, epileptic seizure

Indexed in Scopus

no

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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