title: [Research on individual sleep staging based on principal component analysis and support vector machine].
abstract: The research of sleep staging is an important basis of evaluating sleep quality and diagnosing diseases. In order to achieve automatic sleep staging, we proposed a new method which combines with principal component analysis (PCA) and support vector machine (SVM) for automatic sleep staging. Firstly, we used PCA to reduce dimension of time-frequency-space domains and nonlinear dynamical characteristics of sleep EEG from 5 subjects to reduce data redundancy. Secondly, we used 1-a-1 SVM to classify sleep stages. The results showed that the correct rate can reach 89.9%, which was better than those of many other similar studies.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhou
authors.forename: Xiangxin
authors.initials: X
authors.lastname: Li
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.forename: Dong
authors.initials: D
authors.lastname: Ming
authors.forename: Xinming
authors.initials: X
authors.lastname: Dong
authors.forename: Ranting
authors.initials: R
authors.lastname: Xue
authors.forename: Xuemin
authors.initials: X
authors.lastname: Wang
mesh_terms: D004569:Electroencephalography; D006801:Humans; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis; D012894:Sleep Stages; D060388:Support Vector Machine