A sleep stage classification algorithm based on radial basis function networks

被引：1
|
作者：
Cui, Zhihong [1 ,2 ]
Zheng, Xianwei [1 ,2 ]
机构：
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China
[2] Shandong Prov Key Lab Distributed Comp Software N, Jinan 250014, Shandong, Peoples R China
来源：
PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING ICCSE 2017 | 2017年
基金：
中国国家自然科学基金;
关键词：
Sleep stage classification; Radial basis function neural network; Sleep features;
D O I：
10.1145/3126973.3126976
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Based on the auto-regressive model power spectrum analysis of sleep signal in time-frequency domain, it is found that each sleep stage has its own unique power spectrum in each frequency band. The change of sleep phase is accompanied with the change of sleep signal spectrum. In this paper, we firstly study the original RBF neural network for automatic sleep staging and then propose an improved classification algorithm in which the power spectrum of each sleep stage known as frequency domain features and five another time domain features are calculated as input parameters. The proposed classification algorithm is tested on ISRUC-Sleep data set. Experimental results demonstrate that classification algorithm based on the improved radial basis function network is effective in accuracy and efficiency.
引用
收藏
页码：56 / 60
页数：5
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