Vol.44, No.2, 2023, pp.1517-1529, doi:10.32604/csse.2023.026482
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ARTICLE
Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals
  • Ashit Kumar Dutta1,*, Yasser Albagory2, Manal Al Faraj1, Yasir A. M. Eltahir3, Abdul Rahaman Wahab Sait4
1 Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
2 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Kingdom of Saudi Arabia
3 Department of Respiratory Care, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia
4 Department of Archives and Communication, King Faisal University, Al Ahsa, Hofuf, 31982, Kingdom of Saudi Arabia
* Corresponding Author: Ashit Kumar Dutta. Email:
Received 28 December 2021; Accepted 21 February 2022; Issue published 15 June 2022
Abstract
The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm (COA) and it leads to boosted classification efficiency. In order to ensure the enhanced performance of the OSAE-SSCEEG technique, a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG technique over the recent methods.
Keywords
Biomedical signals; EEG; sleep stage classification; machine learning; autoencoder; softmax; parameter tuning
Cite This Article
A. Kumar Dutta, Y. Albagory, M. Al Faraj, Y. A. M. Eltahir and A. Rahaman Wahab Sait, "Optimal sparse autoencoder based sleep stage classification using biomedical signals," Computer Systems Science and Engineering, vol. 44, no.2, pp. 1517–1529, 2023.
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