基于提升小波与聚类算法的脉象信号识别的研究
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摘要
作为生物医学信号之一的脉象信号能反映人体脉搏的生理与病理信息,对于它的研究有助于人类加深对人体自身的认识,在防病与治病中发挥更大的作用,这是本文进行研究的目的。
小波变换是一个迅速发展的新领域,它是在时域和频域都具有良好局域性的一种信号分析方法,而且非常适用于非平稳信号的处理。但是小波分析不适合非欧氏空间的应用,为了弥补传统小波的一些不足,提升小波应运而生,提升小波不仅具有通用、灵活的特点,而且还有高效的提升实现算法。由于有限长滤波器多相位矩阵的分解不是唯一的,而相同的多相位矩阵的提升实现算法不一定相同,本论文在总结前人工作的基础上,较深入地研究了应用提升小波变换对脉象信号进行特征提取与识别的方法,实现了多相位矩阵两种不同的分解格式及其相应的提升实现算法。其中一种分解格式及其相应的提升实现算法是由本文提出的.然后分别对40例脉象信号(20例健康正常人和20例海洛因吸毒者的脉象信号)进行一级提升小波变换,通过尺度系数,进行特征提取,找出了健康正常人与海洛因吸毒者的脉象信号之间的差异,初步提出了用于划分健康正常人与海洛因吸毒者的判据。
本文的实验结果表明:第一种提升实现算法下18例健康正常人和19例海洛因吸毒者被检测出来,而健康正常人Z01和Z10被误判,海洛因吸毒者B13被误判;第二种提升实现算法下18例健康正常人和20例海洛因吸毒者被检测出来,而健康正常人Z01和Z10被误判。
本论文还详细介绍了模糊C均值聚类的基本概念和理论依据。着重阐述了C均值模糊聚类的实现算法,在对脉象信号进行特征提取的基础上,利用该算法对20例健康正常人和20例海洛因吸毒者的脉象信号进行分类识别,初步聚类后的效果一般,通过标准差准则对误判的样本进一步划分后取得了不错的效果。
As a part of the biomedicalsignals, Pulse signals can reflect the pulse of human physiological and pathological information. Studying it can help us to deepen our understanding on our body, and it could play a great role in disease prevention and treatment. It is the main research purpose in this paper.
Wavelet transform is developing very qucikly in a new areas at present, it is a good signal analytical method both in the time and the frequency domains, especially applicable for non-stationary signal processing. But wavelet tranform is not suitable for non-European space’s application.In order to compensate for the shortcomings of the traditional wavelet,lifting wavelet generates.Lifting wavelet is not only common, flexible, but also it has effective lifting implementing algorithm. As the finite long filter’s decomposition of the multiphase matrix is not unique,and lifting implementing algorithm is not necessarily the same between the same multiphase matrix. Thus, based on previous work, this paper applys lifting wavelet transform to the pulse signals proceeding feature extraction and identification, achieves in two different multiphase matrix decomposition formats and the corresponding lifting implementing algorithm. One of the decomposition formats and the corresponding lifting implementing algorithm is proposed by this paper,then performs lifting wavelet transfom individually to the 40 pulse signals (20 healthy normal persons and 20 heroin addicts), through the scale coefficients, performing feature extraction, the difference between the healthy persons and the heroin addicts can be found,and a primary criterion for measuring the healthy normal persons and the heroin addicts can be obtained.
There are 18 cases of the healthy normal persons and 19 cases of the heroin addicts that are detected in the first lifting algorithm, the healthy normal person Z01 and Z10 are misjudged, and the heroin addict B13 is misjudged, too.Also there are 18 cases of the healthy normal persons and 20 cases of the heroin addicts that are detected in the second lifting algorithm, the healthy person Z01 and Z10 are misjudged. Then the two methords are compared between the advantages and disadvantages.
This paper also gives the basic concepts and theorise of the fuzzy C-means clustering in detail, and expounds the fuzzy C-means clustering algorithm. Based on the feature extraction of the pulse signals, this paper uses the algorithm to identify the pulse signals between th 20 healthy persons and the 20 heroin addicts. And there is a general result at the beginning, then the misjudged samples are devided by the standard deviation and a good result is achieved.
小波变换是一个迅速发展的新领域,它是在时域和频域都具有良好局域性的一种信号分析方法,而且非常适用于非平稳信号的处理。但是小波分析不适合非欧氏空间的应用,为了弥补传统小波的一些不足,提升小波应运而生,提升小波不仅具有通用、灵活的特点,而且还有高效的提升实现算法。由于有限长滤波器多相位矩阵的分解不是唯一的,而相同的多相位矩阵的提升实现算法不一定相同,本论文在总结前人工作的基础上,较深入地研究了应用提升小波变换对脉象信号进行特征提取与识别的方法,实现了多相位矩阵两种不同的分解格式及其相应的提升实现算法。其中一种分解格式及其相应的提升实现算法是由本文提出的.然后分别对40例脉象信号(20例健康正常人和20例海洛因吸毒者的脉象信号)进行一级提升小波变换,通过尺度系数,进行特征提取,找出了健康正常人与海洛因吸毒者的脉象信号之间的差异,初步提出了用于划分健康正常人与海洛因吸毒者的判据。
本文的实验结果表明:第一种提升实现算法下18例健康正常人和19例海洛因吸毒者被检测出来,而健康正常人Z01和Z10被误判,海洛因吸毒者B13被误判;第二种提升实现算法下18例健康正常人和20例海洛因吸毒者被检测出来,而健康正常人Z01和Z10被误判。
本论文还详细介绍了模糊C均值聚类的基本概念和理论依据。着重阐述了C均值模糊聚类的实现算法,在对脉象信号进行特征提取的基础上,利用该算法对20例健康正常人和20例海洛因吸毒者的脉象信号进行分类识别,初步聚类后的效果一般,通过标准差准则对误判的样本进一步划分后取得了不错的效果。
As a part of the biomedicalsignals, Pulse signals can reflect the pulse of human physiological and pathological information. Studying it can help us to deepen our understanding on our body, and it could play a great role in disease prevention and treatment. It is the main research purpose in this paper.
Wavelet transform is developing very qucikly in a new areas at present, it is a good signal analytical method both in the time and the frequency domains, especially applicable for non-stationary signal processing. But wavelet tranform is not suitable for non-European space’s application.In order to compensate for the shortcomings of the traditional wavelet,lifting wavelet generates.Lifting wavelet is not only common, flexible, but also it has effective lifting implementing algorithm. As the finite long filter’s decomposition of the multiphase matrix is not unique,and lifting implementing algorithm is not necessarily the same between the same multiphase matrix. Thus, based on previous work, this paper applys lifting wavelet transform to the pulse signals proceeding feature extraction and identification, achieves in two different multiphase matrix decomposition formats and the corresponding lifting implementing algorithm. One of the decomposition formats and the corresponding lifting implementing algorithm is proposed by this paper,then performs lifting wavelet transfom individually to the 40 pulse signals (20 healthy normal persons and 20 heroin addicts), through the scale coefficients, performing feature extraction, the difference between the healthy persons and the heroin addicts can be found,and a primary criterion for measuring the healthy normal persons and the heroin addicts can be obtained.
There are 18 cases of the healthy normal persons and 19 cases of the heroin addicts that are detected in the first lifting algorithm, the healthy normal person Z01 and Z10 are misjudged, and the heroin addict B13 is misjudged, too.Also there are 18 cases of the healthy normal persons and 20 cases of the heroin addicts that are detected in the second lifting algorithm, the healthy person Z01 and Z10 are misjudged. Then the two methords are compared between the advantages and disadvantages.
This paper also gives the basic concepts and theorise of the fuzzy C-means clustering in detail, and expounds the fuzzy C-means clustering algorithm. Based on the feature extraction of the pulse signals, this paper uses the algorithm to identify the pulse signals between th 20 healthy persons and the 20 heroin addicts. And there is a general result at the beginning, then the misjudged samples are devided by the standard deviation and a good result is achieved.
引文
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[2]宋国乡,甘小冰.数值泛函与小波理论[M].西安:西安电子科技大学出版社,2003.
[3]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005.
[4] Fengli Wang,Deyou Zhao.Local Wave Decomposition Based on Lifting Wavelet Denoising and Its Application[J].Intelligent Computing and Intelligent Systems,2009,3:131-135.
[5] Min Li,Guangpu He,Xiaoying Zhang,Xiaohong Wu.Power Quality Detecting Based on Fast Lifting Wavelet Transform[J].Control and Decision Conference,2008,:536-540.
[6]梁茜.双正交与提升小波的构造方法研究[D].桂林电子科技大学硕士学位论文,2008.
[7]李茗.提升小波仔局部放电信号消噪及模式识别中的应用[D].辽宁科技大学硕士学位论文,2008.
[8]王振飞.提升小波的高效算法设计及其在数字图像处理中的应用[D].华中科技大学博士学位论文,2006.
[9]杨汉生.提升小波仔电能质量分析中的应用研究[D].南京理工大学博士学位论文,2006.
[10]薛坚.提升小波变换的应用与硬件实现[D].南京航空航天大学硕士学位论文,2008.
[11]岳沛平,李训铭.小波变换在中医脉象信号特征分析中的应用[J].南京中医药大学学报,2005,
[12]李墅娜,宋文爱.人体脉象信号特征提取方法的分析研究[J].辽宁中医杂志,2008,
[13]刘文涛.基于小波与模式识别的人体脉象信号客观化研究[D].中北大学硕士学位论文,2008.
[14]彭涛.脉象信号分析与中医脉象证型识别研究[D].苏州大学硕士学位论文,2008.
[15]李玉韩.高阶谱估计在中医脉象信号分析中的应用研究[D].重庆大学硕士学位论文,2005.
[16]曹丁.基于倒谱特征的脉象信号识别算法研究[D].重庆大学硕士学位论文,2009.
[17]周丹.短时傅里叶变换和提升小波变换在脉象信号分析中的应用[D].重庆大学硕士学位论文,2008.
[18]温晓明.脉象信号特征提取与识别的研究[D].重庆大学硕士学位论文,2009.
[19]于子娟.基于模糊C均值聚类的彩色图像分割方法研究[D].中南大学硕士学位论文,2009.
[20]王亚平.基于未确知C均值聚类的绿色品牌诊断研究[D].河北工程大学硕士学位论文,2008.
[21]李丽丽.模糊C-均值聚类算法及其在图像分割中的应用[D].山东师范大学硕士学位论文,2009.
[22]孙轶.基于自适应提升小波的信号消噪技术研究.[D].中国科技大学博士学位论文,2008.
[23]汤小明.提升小波仔心电压缩中的应用[D].西安电子科技大学硕士学位论文,2004.
[24]任先文,余志飞,孙功伟,王坤.基于提升小波的电能质量分析仪研究[J].电力电子技术,2009,43 (10):34-36.
[25]刘长征,黄茂成.快速提升小波变换的研究及其实现[J].微计算机信息(管控一体化),2009,25 (9):32-34.
[26] Tao Huang,Lele Qin.Image Denoising Research Based on Lifting Wavelet Transform and Threshold Optimization[J].Microwave,Antenna,Propagation and EMC Technologies for wireless Communications,2009,3:1218-1220.
[27] Wenbing Fan,Zheng Ge,YaoWang.Adaptive Wiener Filter Based on Fast Lifting Wavelet Transform for Image Enhancement[J].Intelligent Control and automation,2008,3:3633-3636.
[28] Xian-Li Li,Jia-wei Zhang,Wei-hong Fang.THE RESEARCH OF DATA COMPRESSION ALGORITHM BASED on LIFTING WAVELET TRANFORM FOR WIRELESS SENSOR NETWORK[J].Apperceiving Computing and Intelligence Analysis,2009,:228-233.
[29]王春花.基于模糊C-均值聚类的图像分割技术研究[D].辽宁师范大学硕士学位论文,2008.
[30]边肇祺编著.模式识别(第二版)[M].北京:清华大学出版,2004.
[31]王勇.模糊C-均值算法在入侵检测系统中的应用研究[D].哈尔滨理工大学硕士学位论文,2007.
[32]冯梅芳.模糊C-均值算法在彩色牛乳体细胞图像分割中的应用[D].内蒙古农业大学硕士学位论文,2008.
[33]闫兆振.自适应模糊C-均值聚类算法研究[D].山东科技大学硕士学位论文,2006.
[34]赵冬,赵光恒,王天慧.基于模糊C-均值聚类航天图像分割方法的研究[J].宇航学报,2009,30(4):1667-1674.
[35]潘玉娜,陈进,李兴林.基于模糊C-均值的设备性能退还评估方法[J].上海交通大学学报,2009,43(11):1794-1997.
[36]柯珊虹,廖亮,王伟凝,林土胜.FCM与KFCM-II算法在医学MRI图像分割中的应用[J].科学技术与工程,2009,9(22):6687-6693.
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