Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

作     者：Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Digambar Puri;Pramod Kachare;Smith Khare;Ibrahim Al-Shourbaji;Abdoh Jabbari;Abdalla Alameen

作者机构：Department of Electronics and TelecommunicationRamrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller InstituteFaculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics EngineeringJazan UniversityJazan45142Saudi Arabia Department of Computer ScienceUniversity of HertfordshireHatfieldUK Department of Computer Engineering and InformationCollege of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报(英文版))

年 卷 期：2025年第22卷第2期

页      面：884-900页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Not Applicable 

主　　题：Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization 

摘      要：The global incidence of Alzheimer s Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.