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

Authors: Mohamed Akram Khelili; Sihem Slatnia; Okba Kazar; Seyedali Mirjalili; Samir Bourekkache; Guadalupe Ortiz; Yizhang Jiang

Addresses: Department of Computer Science, Smart Computer Science Laboratory, University of Mohamed Khider, Biskra, Algeria ' Department of Computer Science, Smart Computer Science Laboratory, University of Mohamed Khider, Biskra, Algeria ' College of Computing and Informatics, Department of Computer Science, University of Sharjah, United Arab Emirates; College of Arts, Sciences and Information Technology, University of Kalba, Sharjah, United Arab Emirates ' Centre for Artificial Intelligence Research and Optimization, Smart Computer Science Laboratory, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia ' Department of Computer Science, Smart Computer Science Laboratory, University of Mohamed Khider, Biskra, Algeria ' Department of Computer Science and Engineering, UCASE Software Engineering Group, University of Cadiz, Puerto Real, Spain ' School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China

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.

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

DOI: 10.1504/IJMEI.2024.138291

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.3, pp.260 - 273

Received: 30 Aug 2021
Accepted: 26 Feb 2022
Published online: 01 May 2024 *

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