江苏大学学报（自然科学版）2011，Vol.32Issue(3)：325-329,5.DOI:10.3969/j.issn.1671-7775.2011.03.016
基于贝叶斯相关向量机的脑电睡眠分期
Classification of EEG sleep stage based on Bayesian relevance vector machine
摘要
Abstract
To overcome the disadvantages of complicated calculation and uncertain parameter selection of support vector machine (SVM), a new algorithm based on sparse Bayesian relevance vector machine (RVM) was proposed to classify electroencephalography (EEG) sleep stage. Inference and optimization of parameters of the binary classification RVM were given, and binary tree RVM multi-class model was established. According to the known sleep stage annotations by experts, sample entropy (SampEn) features of each sleep stage were extracted from the EEG sleep signals of eight healthy volunteers without any medication in MIT/BIH database. Then the sleep stage types were identified through multi-lay RVM pattern recognition classifier on binary tree categorization by training and testing samples of sleep and awake period. The results show that the maximal identification rate of RVM can reach 89.00％, which is better than that of the SVM (87.67％). The number of relevance vectors and test time of RVM are both less than those of SVM, which means that the RVM method is an effective tool for sleep stage classification with better classification accuracy and computation efficiency.关键词
脑电波/睡眠/相关向量机/支持向量机/样本熵Key words
electroencephalography/ sleep/ relevance vector machine (RVM)/ support vector machine (SVM)/ sample entropy (SampEn)分类
医药卫生引用本文复制引用
沈跃,刘慧,谢洪波,和卫星..基于贝叶斯相关向量机的脑电睡眠分期[J].江苏大学学报（自然科学版）,2011,32(3):325-329,5.基金项目
江苏省自然科学基金资助项目(BK2009198) （BK2009198）
