Sleep EEG staging based on Hilbert-Huang transform and sample entropy

被引：8
|
作者：
Guo Chaozhen [1 ]
Lu Fulan [1 ]
Liu Siyang [1 ]
Xu Weichao [1 ]
机构：
[1] Guangdong Univ Technol, Sch Automat, Guangzhou, Guangdong, Peoples R China
来源：
2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN) | 2015年
关键词：
EEG; Hilbert-Huang transform; sample entropy; sleep stages; SVM; APPROXIMATE ENTROPY;
D O I：
10.1109/CICN.2015.92
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a sleep EEG staging method based on Hilbert-Huang transform and sample entropy was proposed to improve the efficiency of automatic sleep staging. Firstly, the marginal spectrum of EEG was obtained by Hilbert-Huang transform. Secondly, features were extracted based on the energy distribution of the frequency band and EEG sample entropy. Thirdly, the extracted features were employed to construct an SVM classifier. Experiment results based on 10 subjects showed that the average sleep staging accuracy was 89.9%, which verified the effectiveness of the proposed method.
引用
收藏
页码：442 / 445
页数：4
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