Electrooculogram based Sleep Stage Classification Using Deep Belief Network

被引：0

作者：

Xia, Bin ^{[1
,2
]}

Li, Qianyun ^{[2
]}

Jia, Jie ^{[3
]}

Wang, Jingyi ^{[1
]}

Chaudhary, Ujwal ^{[1
]}

Ramos-Murguialday, Ander ^{[1
,5
]}

Birbaumer, Niels ^{[1
,4
]}

机构：

[1] Univ Tubingen, Inst Med Psychol & Behav Neurobiol, D-72076 Tubingen, Germany

[2] Shanghai Maritime Univ, Dept Elect Engn, Shanghai 201306, Peoples R China

[3] Fudan Univ, Hua Shan Hosp, Dept Rehabil Med, Shanghai 200040, Peoples R China

[4] Osped San Camillo IRCCS, Ist Ricovero & Cura Carattere Sci, I-30126 Venice, Lido, Italy

[5] TECNALIA, San Sebastian 20009, Spain

来源：

2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2015年

关键词：

deep belief network; automatic sleep stage classification; EOG; Hidden Markov Models;

D O I：

暂无

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 work, we used single electrooculogram (EOG) signal to perform automatic sleep scoring. Deep belief network (DBN) and combination of DBN and Hidden Markov Models (HMM) are employed to discriminate sleep stages. Under the leave-one-out protocol, the average accuracy of DBN and DBN-HMM are 77.7% and 83.3% for all sleep stages, respectively. On the other hand, we found the EOG signal not only contribute to identify stages of Awake and rapid eye movement, also contribute to discriminate stage 2 and slow wave sleep stage.

引用

页数：5

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