Research on individual sleep staging based on principal component analysis and support vector machine / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1176-1179, 2013.
Article
Dans Chinois
| WPRIM
| ID: wpr-259745
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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Phases du sommeil
/
Dynamique non linéaire
/
Analyse en composantes principales
/
Électroencéphalographie
/
Machine à vecteur de support
Type d'étude:
Étude pronostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2013
Type:
Article
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