Research on individual sleep staging based on principal component analysis and support vector machine.
- Author:
Peng ZHOU
;
Xiangxin LI
;
Yi ZHANG
;
Dong MING
;
Xinming DONG
;
Ranting XUE
;
Xuemin WANG
- Publication Type:Journal Article
- MeSH:
Electroencephalography;
Humans;
Nonlinear Dynamics;
Principal Component Analysis;
Sleep Stages;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2013;30(6):1176-1179
- CountryChina
- Language:Chinese
-
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.
