TY  - JOUR
SN  - 20010370
VL  - 27
TI  - Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals
ID  - unic17890
KW  - EEG signals
Brain activity
Epileptic seizures
Transfer learning
Explainable AI
SP  - 5182
Y1  - 2025/11//
A1  - K?na, Erol
A1  - Raza, Ali
A1  - Are, Prudhvi Chowdary
A1  - Rodríguez Velasco, Carmen Lilí
A1  - Brito Ballester, Julién
A1  - Diez, Isabel de la Torre
A1  - Butt, Naveed Anwer
A1  - Ashraf, Imran
AV  - public
N2  - Epileptic seizures are neurological events characterized by sudden and excessive electrical discharges in the brain, leading to disruptions in brain function. Epileptic seizures can lead to life-threatening situations such as status epilepticus, which is characterized by prolonged or recurrent seizures and may lead to respiratory distress, aspiration pneumonia, and cardiac arrhythmias. Therefore, there is a need for an automated approach that can efficiently diagnose epileptic seizures at an early stage. The primary objective of this study is to develop a highly accurate approach for the early diagnosis of epileptic seizures. We use electroencephalography (EEG) signal data based on different brain activities to conduct experiments for epileptic seizure detection. For this purpose, a novel transfer learning technique called random forest-gated recurrent unit (RFGR) is proposed. The EEG brain activity signal data is fed into the RFGR model to generate a new feature set. The newly generated features are based on the class prediction probabilities extracted by the RFGR and are utilized to train models. Extensive experiments are carried out to investigate the performance of the proposed approach. Results demonstrate that the RFGR, when used with the random forest model, outperforms state-of-the-art techniques, achieving a high accuracy of 99.00 %. Additionally, explainable artificial intelligence analysis is utilized to provide transparent and understandable explanations of the decision-making processes of the proposed approach.
EP  - 5193
JF  - Computational and Structural Biotechnology Journal
UR  - http://doi.org/10.1016/j.csbj.2025.10.054
ER  -