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 multiclass 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 multilay 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.
2011, Vol. 32 
