[1]李斐,马千里. 基于脑电信号特征提取的睡眠分期方法研究[J].计算机技术与发展,2017,27(01):177-181.
　LI Fei,MA Qian-li. Research on Sleep Staging Method Based on Feature Extraction of EEG[J].,2017,27(01):177-181.
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 基于脑电信号特征提取的睡眠分期方法研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年01期
页码:
177-181
栏目:
应用开发研究
出版日期:
2017-01-10

文章信息/Info

Title:
 Research on Sleep Staging Method Based on Feature Extraction of EEG
文章编号:
1673-629X（2017)01-0177-05
作者:
 李斐; 马千里
 南京邮电大学 通信与信息工程学院
Author(s):
 LI Fei; MA Qian-li
关键词:
 ; 睡眠分期;  脑电信号; 小波包系数; 排列熵; Petrosian分形维数
Keywords:
 ; sleep stage; EEG; wavelet packet coefficient; permutation entropy; Petrosian fractal dimension
分类号:
TP301
文献标志码:
A
摘要:
 睡眠分期是研究睡眠其及相关疾病的基础,是完成睡眠质量评估的前提,具有重要的意义。主要提出了一种新的脑电信号特征提取方法,能够有效提高睡眠分期的准确率。传统的自动睡眠分期方法都是以一个睡眠时期的时间片为单位进行特征参数提取,文中的睡眠分期按每30 s进行一次睡眠时期判断,将特征提取的时间片分为30 s、90 s、150 s以及210 s,研究不同时间片提取的特征参数对睡眠分期准确率的影响。采用Weka工具中的随机森林分类器对睡眠状态进行判别。实验结果表明,将210 s时间片得到的小波包系数、30 s时间片得到的排列熵以及90 s时间片得到的Petrosian分形维数作为自动睡眠分期的参数,可以得到85%的准确率；而采用30 s时间片得到的以上三类参数只能达到65%的准确率。
Abstract:
 Researches on sleep staging is not only the basis of diagnosing sleep related diseases,but also the precondition of sleep quality evaluation,which has vital significance. A new method to extract EEG features is proposed which effectively improves the accuracy of sleep staging. Different from traditional automatic sleep staging method,sleep stage is classified every 30 seconds and time slice for feature extraction is respectively divided into 30 seconds,90 seconds,150 seconds and 210 seconds to study characteristic parameters of difference time slices on the accuracy of sleep stage. Besides,a random forest classifier in Weka tools is adopted to identify sleep state. The result shows that putting wavelet packet coefficients obtained by the 210 s time slice,the permutation entropy from the 30 s time slice and the Petrosian fractal dimension from 90 s time slice as the parameters of the automatic sleep staging,it can get accuracy of 85%,while three kinds of parameters in 30 s time slice above can only reach accuracy of 65%.

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更新日期/Last Update: 2017-04-05