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New bio-inspired approach for deep learning techniques applied to neonatal seizures

  • Mohamed Akram Khelili
  • , Sihem Slatnia
  • , Okba Kazar
  • , Seyedali Mirjalili
  • , Samir Bourekkache
  • , Guadalupe Ortiz
  • , Yizhang Jiang
  • University of Biskra
  • Torrens University Australia
  • University of Cádiz
  • Jiangnan University

Research output: Contribution to journalArticlepeer-review

Abstract

Neonatal seizures are a common emergency in the neonatal intensive care unit and their detection using electroencephalography (EEG) recording is one of the biggest challenges that neurologists face. Even though using artificial intelligence methods such as deep learning for computer vision can help to solve these problems, time consumption, complexity, and overfitting or underfitting of the model still limit the application of deep learning. In order to produce a real-time system that can detect neonatal seizures using EEG and solve the problem of the lack of availability of neurologists, a convolution neural network-based marine predator algorithm system is proposed.

Original languageEnglish
Pages (from-to)260-273
Number of pages14
JournalInternational Journal of Medical Engineering and Informatics
Volume16
Issue number3
DOIs
StatePublished - 2024

Keywords

  • artificial intelligence
  • CNN
  • convolution neural network
  • deep learning
  • EEG
  • electroencephalography
  • genetic algorithm
  • marine predators algorithm
  • MPA
  • neonatal seizures
  • parallel metaheuristic optimisation

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