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 

作者机构：Department of Computer Science and Information SystemsCollege of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer EngineeringCollege of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Respiratory CareCollege of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and CommunicationKing Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1517-1529页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

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

主　　题：Biomedical signals EEG sleep stage classification machine learning autoencoder softmax parameter tuning 

摘      要：The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical ***,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG *** major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical *** OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization ***,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)***,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 effi*** 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.