[1]严文娟,李刚,林凌.基于近红外光谱的舌诊疾病识别的研究[J].红外技术,2010,32(8):487-490.
 YAN Wen-juan,LI Gang,LIN Ling.Research on Discrimination of Tongue Diseases with Near Infrared Spectroscopy[J].Infrared Technology,2010,32(8):487-490.
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基于近红外光谱的舌诊疾病识别的研究
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
32卷
期数:
2010年第8期
页码:
487-490
栏目:
出版日期:
2010-08-20

文章信息/Info

Title:
Research on Discrimination of Tongue Diseases with Near Infrared Spectroscopy
文章编号:
1001-8891(2010)08-0487-04
作者:
严文娟12李刚2林凌2
1. 长江师范学院物理学与电子工程学院,重庆 408100;2. 天津大学天津市生物医学检测技术与仪器重点实验室,天津 300072
Author(s):
YAN Wen-juan12LI Gang2LIN Ling2
1. School of Physics&Electron Engineering, Yangtze Normal University, Chongqing 408100, China;
2. Tianjin University,Tianjin Key Laboratory of Biomedical Detecting Techniques & Instruments, Tianjin 300072, China

关键词:
近红外光谱舌诊主成分分析广义回归神经网络
Keywords:
near infrared spectroscopytongue diagnosisprincipal component analysisgeneral regression neural network
分类号:
O433.4
文献标志码:
A
摘要:
为了对中医舌诊的客观化研究,提出了应用近红外光谱分析技术快速无创的对健康人、冠心病、糖尿病和肝炎患者的不同人群的舌诊近红外光谱进行识别的新方法。首先对98个样本光谱数据进行归一化处理,用主成分分析(PCA)方法得出的累计贡献率达99.88%的前8个主成分作为广义回归神经网络(GRNN)的输入变量,建立了舌诊近红外光谱的识别模型。利用该模型分别选取了18个不同人群的近红外光谱数据共72个样本用于神经网络的训练,余下的26个用于预测,当光滑因子为5/8时预测的最大误差为0.17342,最小误差为0,获得了较理想的预测精度。实验结果表明用PCA和GRNN相结合的方法对舌诊近红外光谱与疾病之间建立了较好的关联,对加强中医舌诊的客观化起到了很好的促进作用,为疾病的诊断提供了一种新的方法。
Abstract:
To make an objective research on tongue diagnosis of Tradition Chinese Medicine, it is proposed that spectroscopy analytical technique which is non-invasive discrimination tongue spectra of different groups who are healthy people and coronary, diabetic, hepatic patients. Firstly, normalize 98 samples of spectral data, next use principal component analysis to gain the first 8 principal components derived from cumulative contribution rate of 99.88%, then take the first 8 principal components as the general regression neural network input variables to establish a spectral discrimination of tongue model. It is selected according to the model that a total of 72 samples from 18 different groups of spectral data were used for neural network training, the remaining 26 used to predict. When the smooth factor is 5/8, the maximum error of prediction is 0.17342, the smallest error is 0, a more satisfactory prediction accuracy can be obtained. Experimental results show that the combination of PCA and GRNN, which reveals a good association between tongue diagnosis and disease spectrum, plays a good role in objectifying tongue diagnosis of Tradition Chinese Medicine, and provides a new approach for diagnosis.

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备注/Memo

备注/Memo:
收稿日期:2010-04-07.
作者简介:严文娟(1976-),女,硕士,讲师,主要研究方向为信号检测与处理、模式识别。E-mail:yanwj76@163.com
通讯作者:林凌,女,博士,副教授,主要从事信号检测与处理、生物医学工程的研究工作。E-mail:linling@tju.edu.cn.
基金项目:国家自然科学基金项目,编号:30973964。

更新日期/Last Update: 2014-04-28