基于支持向量机的睡眠结构分期研究

计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (8): 5-8.

基于支持向量机的睡眠结构分期研究

葛家怡¹,周鹏¹,赵欣¹,刘海婴^1,2,王明时¹

1.天津大学精密仪器与光电子工程学院，天津 300072
2.明尼苏达大学放射系，美国

收稿日期:2007-10-29 修回日期:2007-12-06 出版日期:2008-03-11 发布日期:2008-03-11
通讯作者: 葛家怡

Study of sleep architecture stage based on Support Vector Machines

GE Jia-yi¹,ZHOU Peng¹,ZHAO Xin¹,LIU Hai-ying^1,2,WANG Ming-shi¹

1.College of Precision Instrument and Optoelectronics Engineering，Tianjin University，Tianjin 300072，China
2.Department of Radiology，University of Minnesota，USA

Received:2007-10-29 Revised:2007-12-06 Online:2008-03-11 Published:2008-03-11
Contact: GE Jia-yi

摘要/Abstract

摘要： 为了提高睡眠结构分期的准确度，克服分类时样本不足对分类的影响，使用MIT-BIH数据库整晚睡眠脑电数据作为研究样本，提取了时域、频域和非线性共16个参数作为分类特征，用支持向量机的一对一多类分类方法，采用顺序最小优化算法，以径向基函数作为核函数对样本分类。分类结果与专家的分类标注对比，分类准确率达到92%以上。支持向量机可作为睡眠分期的一种实用算法。

Abstract: In order to increase the accuracy of sleep architecture stage and overcome the influence in classification brought by samples shortage，all night sleep EEG data from MIT-BIH database are accepted as the research sample and totally 16 parameters including time-domain，frequency-domain and nonlinear picked up for sleep classification using 1-against-1 multi-classification method of Support Vector Machines using Sequential Minimal Optimization algorithm and selecting radial basis function as the kernel function.In contrast to expert’s manually-scored classification label，the classification result is more than 92%.It shows that Support Vector Machines can be a kind of effective algorithm in sleep stage.

葛家怡¹,周鹏¹,赵欣¹,刘海婴^1,2,王明时¹. 基于支持向量机的睡眠结构分期研究[J]. 计算机工程与应用, 2008, 44(8): 5-8.

GE Jia-yi¹,ZHOU Peng¹,ZHAO Xin¹,LIU Hai-ying^1,2,WANG Ming-shi¹. Study of sleep architecture stage based on Support Vector Machines[J]. Computer Engineering and Applications, 2008, 44(8): 5-8.