eprintid: 17890 rev_number: 11 eprint_status: archive userid: 2 dir: disk0/00/01/78/90 datestamp: 2025-12-12 23:32:20 lastmod: 2025-12-12 23:32:22 status_changed: 2025-12-12 23:32:20 type: article metadata_visibility: show creators_name: Kına, Erol creators_name: Raza, Ali creators_name: Are, Prudhvi Chowdary creators_name: Rodríguez Velasco, Carmen Lilí creators_name: Brito Ballester, Julién creators_name: Diez, Isabel de la Torre creators_name: Butt, Naveed Anwer creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: carmen.rodriguez@uneatlantico.es creators_id: julien.brito@uneatlantico.es creators_id: creators_id: creators_id: title: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: EEG signals Brain activity Epileptic seizures Transfer learning Explainable AI abstract: 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. date: 2025-11 publication: Computational and Structural Biotechnology Journal volume: 27 pagerange: 5182-5193 id_number: doi:10.1016/j.csbj.2025.10.054 refereed: TRUE issn: 20010370 official_url: http://doi.org/10.1016/j.csbj.2025.10.054 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Artículos y libros Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Kına, Erol; Raza, Ali; Are, Prudhvi Chowdary; Rodríguez Velasco, Carmen Lilí; Brito Ballester, Julién; Diez, Isabel de la Torre; Butt, Naveed Anwer y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carmen.rodriguez@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals. Computational and Structural Biotechnology Journal, 27. pp. 5182-5193. ISSN 20010370 document_url: http://repositorio.unic.co.ao/id/eprint/17890/1/PIIS2001037025004581.pdf