TY  - EJOU
AU  - Hilal, Anwer Mustafa 
AU  - Al-Rasheed, Amal 
AU  - Alzahrani, Jaber S. 
AU  - Eltahir, Majdy M. 
AU  - Duhayyim, Mesfer Al 
AU  - Salem, Nermin M. 
AU  - Yaseen, Ishfaq 
AU  - Motwakel, Abdelwahed 

TI  - Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification
T2  - Computer Systems Science and Engineering

PY  - 2023
VL  - 45
IS  - 2
SN  - 

AB  - 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%.
KW  - Signal processing; EEG signals; sleep stage classification; clstm model; deep learning; cmvo algorithm

DO  - 10.32604/csse.2023.030603