中国组织工程研究

中国组织工程研究 ›› 2012, Vol. 16 ›› Issue (44): 8251-8255.doi: 10.3969/j.issn.2095-4344.2012.44.016

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

基于脉搏传感测值和主成分分析对精神疲劳状态的识别

任亚莉   

  1. 陇东学院电气工程学院，甘肃省庆阳市 745000
  • 收稿日期:2012-03-08 修回日期:2012-04-15 出版日期:2012-10-28 发布日期:2012-10-28
  • 作者简介:任亚莉★，女，1970年生，甘肃省庆阳市人，汉族，2007年兰州理工大学毕业，硕士，副教授，主要从事生物医学信号检测分析研究。 renyali888@sohu.com

Detection of mental fatigue state using pulse sensor measurement values and principal component analysis

Ren Ya-li   

  1. Electrical Engineering College, Longdong University, Qingyang 745000, Gansu Province, China
  • Received:2012-03-08 Revised:2012-04-15 Online:2012-10-28 Published:2012-10-28
  • About author:Ren Ya-li★, Master, Associate professor, Electrical Engineering College, Longdong University, Qingyang 745000, Gansu Province, China renyali888@sohu.com

PDF

554

被引次数

6

摘要：

背景：随着科技的进步，研究疲劳的客观手段越来越多，生理指标的介入使其成为医学、认知科学和心理学的研究热点。然而，对精神疲劳的检测目前仍缺乏客观的生理指标。
目的：为了评估精神疲劳状态，提出一种基于脉搏信号的精神疲劳状态识别新方法。
方法：用小波变换对脉搏信号消噪处理，提取脉搏信号功率谱峰值及对应频率、功率谱重心及重心频率特征量，对提取的特征量进行主成分分析，最后用改进的线性判别式分析法分类识别，主成分识别率达100%。
结果与结论：用脉搏信号特征的主成分进行精神疲劳状态识别，获得了满意的分类识别效果，该方法计算简单，稳定性好，识别率高，对精神疲劳状态的评估具有一定的可行性。

关键词: 精神疲劳, 脉搏信号, 主成分分析, 小波变换, 线性判别式分析, 数字医学

Abstract:

BACKGROUND: With the development of science and technology, there are more and more objective methods to research fatigue, and physiological indexes intervene makes it a medical, cognitive science and psychology research hotspot. However, the detection of mental fatigue is still lack of objective physiological indexes.
OBJECTIVE: To propose a novel sub-health state recognition method based on principal component analysis and pulse signal features is presented in order to evaluate mental fatigue state.
METHODS: Firstly, wavelet transform was used to de-noise the pulse signal. Secondly, the peak value of power spectrum, features of corresponding frequency, center of gravity and gravity frequency of power spectrum were extracted. Thirdly, the extracted features were performed with principal component analysis. Finally, an improved linear discriminant analysis was applied to classification and pattern recognition. The results demonstrated that the recognition ratio of the principal component was up to 100%.
RESULTS AND CONCLUSION: This method possessed many attractive characters such as simpler calculated process, better stability, and could get higher recognition rate, which provides a certain reference value for achieving detection of mental fatigue state.

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