Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals  

作　　者：Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Yasir A.M.Eltahir Abdul Rahaman Wahab Sait 

机构地区：[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

出　　处：《Computer Systems Science & Engineering》2023年第2期1517-1529,共13页计算机系统科学与工程（英文）

基　　金：Taif University Researchers Supporting Project Number(TURSP-2020/161);Taif University,Taif,Saudi Arabia.

摘　　要：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 tofind 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 tech-nique over the recent methods.

关 键 词：Biomedical signals EEG sleep stage classification machine learning autoencoder softmax parameter tuning 

分 类 号：TN762[电子电信—电路与系统]