电子学报2017，Vol.45Issue(1)：225-231,7.DOI:10.3969/j.issn.0372-2112.2017.01.031
基于序列连通度的睡眠分期算法研究
Sleep Staging from the Visibility Graph Algorithm of Series
摘要
Abstract
Monitoring the sleep quality accurately can play an effective supporting role in helping people improve the quality of sleep.In the present study,a novel feature extraction algorithm is proposed based on the natural visibility graph and horizontal visibility graph methods.The slope of visibility degree distribution,the mean of visibility distance,the mean of averaged visibility distance and the mean of improved weighted visibility graph were extracted,and trained by the least square-support vector machines (LS-SVM) classifier.The mathematical model between electroencephalogram (EEG) and sleep state was established and verified by different samples.The results demonstrated that the classification accuracy of different states improved about 5.72％ compared to the existing weighted visibility graph,the classification accuracy of shallow sleep states improved about 9.65 ％.关键词
脑电信号/序列连通度/最小二乘支持向量机Key words
EEG(Electroencephalogram)/visibility graph/LS-SVM (Least square-support vector machines)分类
医药卫生引用本文复制引用
刘志勇,孙金玮..基于序列连通度的睡眠分期算法研究[J].电子学报,2017,45(1):225-231,7.基金项目
哈尔滨工业大学理工医交叉学科基础研究培育计划(No.HIT.IBRSEM.2013005) （No.HIT.IBRSEM.2013005）
哈尔滨市科技创新人才研究专项资金(No.2015RAXXJ038) （No.2015RAXXJ038）
