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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: email

Computer Systems Science and Engineering 2023, 44(2), 1517-1529. https://doi.org/10.32604/csse.2023.026482

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.

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APA Style
Dutta, A.K., Albagory, Y., Faraj, M.A., Eltahir, Y.A.M., Sait, A.R.W. (2023). Optimal sparse autoencoder based sleep stage classification using biomedical signals. Computer Systems Science and Engineering, 44(2), 1517-1529. https://doi.org/10.32604/csse.2023.026482
Vancouver Style
Dutta AK, Albagory Y, Faraj MA, Eltahir YAM, Sait ARW. Optimal sparse autoencoder based sleep stage classification using biomedical signals. Comput Syst Sci Eng. 2023;44(2):1517-1529 https://doi.org/10.32604/csse.2023.026482
IEEE Style
A.K. Dutta, Y. Albagory, M.A. Faraj, Y.A.M. Eltahir, and A.R.W. Sait "Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals," Comput. Syst. Sci. Eng., vol. 44, no. 2, pp. 1517-1529. 2023. https://doi.org/10.32604/csse.2023.026482



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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