Abstract

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Title:: Sleep staging using support vector machine optimized by improved artificial bee colony

Author(s):: XIONG Xin1; WU Di1; ZHANG Yaru1; FENG Jiannan1; YI Sanli1; WANG Chunwu2; LIU Ruixiang3; HE Jianfeng1; 1. College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China 2. School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou 521000, China 3. Department of Clinical Psychology, Yunnan Second Peoples Hospital, Kunming 650021, China

Keywords:: Keywords: sleep staging improved artificial bee colony algorithm support vector machine ReliefF Lévy flight

Abstract:: A support vector machine (IMABC-SVM) optimized by an improved artificial bee colony algorithm is used to for sleep staging. The data components obtained by discrete wavelet transform, time-domain features, non-linear features and micro-state features are filtered using ReliefF algorithm for obtaining the optimal feature matrix which is then trained by IMABC-SVM classifier. Some ablation experiments are conducted to verify the effectiveness of the feature selection and the optimized classifier. The experimental results show that the accuracy of IMABC-SVM reaches 89.97%. IMABC-SVM can provide a basis for the detection, prevention and treatment of sleep-related disorders.