中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (26): 4845-4849.doi: 10.3969/j.issn.1673-8225.2011.26.024

• 数字化骨科 digital orthopedics • 上一篇    下一篇

计算机处理睡眠数据中2导脑电和1导眼电的非周期波形分析

王国锋，彭小虎   

  1. 湖南第一师范学院教育科学系，湖南省长沙市410002
  • 收稿日期:2011-03-20 修回日期:2011-04-15 出版日期:2011-06-25 发布日期:2011-06-25
  • 作者简介:王国锋★，女，1973年生，湖南省岳阳市人，汉族，2005年湖南师范大学毕业，硕士，讲师，主要从事心理学研究。 wang_guo_feng@163.com
  • 基金资助:

    湖南省教育厅研究项目(09C245，09A018)。

Computer processing sleep data of two-channel electroencephalogram and one-channel electrooculogram by aperiodic waveform analysis

Wang Guo-feng, Peng Xiao-hu   

  1. Department of Educational Science, Hunan First Normal University, Changsha  410002, Hunan Province, China
  • Received:2011-03-20 Revised:2011-04-15 Online:2011-06-25 Published:2011-06-25
  • About author:Wang Guo-feng★, Master, Lecturer, Department of Educational Science, Hunan First Normal University, Changsha 410002, Hunan Province, China wang_guo_feng@163.com
  • Supported by:

    Hunan Provincial Education Bureau, No. 09C245*, 09A018*

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摘要：

背景：睡眠中记录的数据量很大，不用计算机自动处理不能满足实用需要，而现有的对睡眠数据进行分期的方法准确率都不高。
目的：考察仅依据脑电与眼电，基于非周期波形分析和径向基函数遗传神经网络的睡眠数据分期新方法。
方法：实验数据来自MIT的PhysioBank中的S1eep-EDF数据库，共8名被试，各记录2导脑电和1导眼电。原始数据经零相位数字滤波后，进行非周期波形分析，得出每个Epoch的特征向量，经预处理后送遗传径向基函数。神经网络配合专家手工分类结果进行训练，训练好的神经网络再对测试数据进行分析。
结果与结论：总的分期符合率为95.6%，超出已知文献研究结果(70%~90%)，具有很高的实用价值，能满足睡眠研究与临床使用。

关键词: 睡眠, 脑电, 自动分期, 神经网络, 非周期波形分析, 遗传算法

Abstract:

BACKGROUND: The sleep data are very large, and it is not satisfied with practice demand if the data cannot process by computer. However, the methods which are using at present have a disadvantage that the accuracy is comparatively low.
OBJECTIVE: To investigate a new method for sleep stage classification only using electroencephalogram (EEG) and electrooculogram (EOG) based on aperiodic waveform analysis and genetic neural network of radial basis function (RBF).
METHODS: Raw data including two-channel EEG and one-channel EOG recorded from eight subjects were obtained from Sleep-EDF database of PhysioBank, MIT. After digital filter with zero phase, raw data were analyzed by aperiodic waveform analysis to extract several parameters that were necessary for sleep stage classification. Then, preprocessed data as input for genetic neural network of RBF accepted training. Finally, test data were sent to trained neural network to validate.
RESULTS AND CONCLUSION: The results obtained, on average 95.6% of agreement between the expert and the GA-ANN for six stages of vigilance, going beyond results of known literature (70%-90%), which possess high value in practice and maybe satisfy with research and clinical application.

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