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

被引：0

作者：

Hilal A.M. ^{[1
]}

Al-Rasheed A. ^{[2
]}

Alzahrani J.S. ^{[3
]}

Eltahir M.M. ^{[4
]}

Al Duhayyim M. ^{[5
]}

Salem N.M. ^{[6
]}

Yaseen I. ^{[1
]}

Motwakel A. ^{[1
]}

机构：

[1] Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj

[2] Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh

[3] Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University

[4] Department of Information Systems, College of Science & Art at Mahayil, King Khalid University

[5] Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University

[6] Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo

来源：

Computer Systems Science and Engineering | 2023年 / 45卷 / 02期

基金：

英国科研创新办公室;

关键词：

clstm model; cmvo algorithm; deep learning; EEG signals; Signal processing; sleep stage classification;

D O I：

10.32604/csse.2023.030603

中图分类号：

学科分类号：

摘要：

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%. © 2023 CRL Publishing. All rights reserved.

引用

页码：1249 / 1263

页数：14

共 23 条

[1]

Mousavi S., Afghah F., Acharya U. R., SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach, PLOS ONE, 14, 5, (2019)

[2]

Korkalainen H., Aakko J., Nikkonen S., Kainulainen S., Leino A., Et al., Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea, IEEE Journal of Biomedical and Health Informatics, 24, (2019)

[3]

Neng W., Lu J., Xu L., CCRRSleepNet: A hybrid relational inductive biases network for automatic sleep stage classification on raw single-channel eeg, Brain Sciences, 11, 4, (2021)

[4]

Zhao R., Xia Y., Wang Q., Dual-modal and multi-scale deep neural networks for sleep staging using EEG and ECG signals, Biomedical Signal Processing and Control, 66, 11, (2021)

[5]

Khalili E., Asl B. M., Automatic sleep stage classification using temporal convolutional neural network and new data augmentation technique from raw single-channel eeg, Computer Methods and Programs in Biomedicine, 204, 3, (2021)

[6]

Sun W., Chen X., Zhang X. R., Dai G. Z., Chang P. S., Et al., A multi-feature learning model with enhanced local attention for vehicle re-identification, Computers, Materials & Continua, 69, 3, pp. 3549-3560, (2021)

[7]

Sun W., Zhang G. C., Zhang X. R., Zhang X., Ge N. N., Fine-grained vehicle type classification using lightweight convolutional neural network with feature optimization and joint learning strategy, Multimedia Tools and Applications, 80, 20, pp. 30803-30816, (2021)

[8]

Satapathy S. K., Bhoi A. K., Loganathan D., Khandelwal B., Barsocchi P., Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal, Biomedical Signal Processing and Control, 69, 1, (2021)

[9]

You Y., Zhong X., Liu G., Yang Z., Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features, Artificial Intelligence in Medicine, 127, 11, (2022)

[10]

Yang B., Zhu X., Liu Y., Liu H., A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model, Biomedical Signal Processing and Control, 68, 2, (2021)

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