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primary topic: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals

Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals
type: AcademicArticle
type: Article
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
volume: 27
is part of: Computational and Structural Biotechnology Journal
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Texto)
has document: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Otro)
subject: Engineering

Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Texto)
type: Document
has file: PIIS2001037025004581.pdf
has part: PIIS2001037025004581.pdf

Computational and Structural Biotechnology Journal
type: Collection
issn: 20010370

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Naveed Anwer Butt
type: Person
given name: Naveed Anwer
family name: Butt

Carmen Lilí Rodríguez Velasco
type: Person
given name: Carmen Lilí
family name: Rodríguez Velasco

Ali Raza
type: Person
given name: Ali
family name: Raza

Julién Brito Ballester
type: Person
given name: Julién
family name: Brito Ballester

Isabel de la Torre Diez
type: Person
given name: Isabel de la Torre
family name: Diez

Erol Kına
type: Person
given name: Erol
family name: Kına

Prudhvi Chowdary Are
type: Person
given name: Prudhvi Chowdary
family name: Are

Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Otro)
type: Document
is version of: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Texto)
is volatile version of: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Texto)
is index codes version of: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17890 Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals
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primary topic: Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals

Engineering
type: Concept
preferred label: Ingeniería @es