东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (9): 1226-1229.DOI: 10.12068/j.issn.1005-3026.2017.09.003

• 信息与控制 • 上一篇    下一篇

基于极限学习机的中医脉象识别方法

陈星池， 黄淑春， 赵海， 王晓漫   

  1. (东北大学 计算机科学与工程学院， 辽宁 沈阳110169)
  • 收稿日期:2016-03-06 修回日期:2016-03-06 出版日期:2017-09-15 发布日期:2017-09-08
  • 通讯作者: 陈星池
  • 作者简介:陈星池(1987-)，男，辽宁绥中人，东北大学博士研究生; 赵海(1959-)，男，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61101121); 辽宁省科学技术计划项目(2015401039); 沈阳市科技专项项目(F15-199-1-03).

Recognition Method of Traditional Chinese Medicine Pulse Conditions Based on Extreme Learning Machine

CHEN Xing-chi， HUANG Shu-chun， ZHAO Hai， WANG Xiao-man   

  1. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China.
  • Received:2016-03-06 Revised:2016-03-06 Online:2017-09-15 Published:2017-09-08
  • Contact: CHEN Xing-chi
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摘要： 针对中医脉象模糊性强、种类繁多、特征复杂的特点，以及传统模糊聚类方法、BP神经网络识别方法的不足，提出了一种基于极限学习机(extreme learning machine，ELM)的脉象识别方法.该方法通过提取脉象信号的特征向量，然后利用ELM对特征向量进行了训练和分类.实验结果表明，本文所提出的脉象识别方法与传统模糊聚类方法、BP神经网络方法和支持向量机方法相比，识别正确率分别提高21%，9%和5%.这表明所提出的方法对脉象的分类判别能取得良好的效果.

关键词: 中医脉象, 脉搏波, 特征提取, 极限学习机, 脉象识别

Abstract: In the light of the ambiguity， variety， and complexity of traditional Chinese medicine(TCM) pulse conditions， and the shortcomings of traditional fuzzy cluster methods and backpropagation(BP) neural network methods， a novel method using the extreme learning machine(ELM) was proposed to detect the pulse conditions. This method identifies pulse condition by using the ELM to train and classify the characteristic vectors obtained by the pulse condition. The experimental results show that comparing with the traditional fuzzy cluster methods， BP neural network method and support vector machine method， the accuracy of proposed method is respectively increased by 21 percent， 9 percent and 5 percent， which shows that this is a better pulse condition estimation using proposed method.

Key words: traditional Chinese medicine pulse condition, pulse wave, pulse characteristics extraction, extreme learning machine (ELM), pulse conditions identification

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