Open Access

ARTICLE

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Anwer Mustafa Hilal^1,*, Amal Al-Rasheed², Jaber S. Alzahrani³, Majdy M. Eltahir⁴, Mesfer Al Duhayyim⁵, Nermin M. Salem⁶, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
4 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Saudi Arabia
6 Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 45(2), 1249-1263. https://doi.org/10.32604/csse.2023.030603

Received 29 March 2022; Accepted 18 May 2022; Issue published 03 November 2022

Abstract

Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts’ knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

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Keywords

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