基于小波变换的脉象信号特征提取方法  

Feature Extraction Methods for Pulse Signal Based on Wavelet Transform

作　　者：张丽琼[1] 王炳和[2] 

机构地区：[1]武警工程学院电子技术系,西安710086 [2]武警工程学院通信工程系,西安710086

出　　处：《数据采集与处理》2004年第3期323-328,共6页Journal of Data Acquisition and Processing

基　　金：陕西省自然科学基金 ( 2 0 0 1 X2 8)资助项目。

摘　　要：为了较好地区分正常人与心脏病人的脉象信号 ,利用小波变换奇异性检测功能与多尺度分辨特性 ,提出了两种提取脉象信号特征的方法 :连续小波变换法和二进小波变换法。在此基础上 ,构造了两种特征向量 :小波变换系数的尺度——主波峰值和小波变换的尺度——能量值。经过对临床采集的 2 3 5例脉象信号的处理与分析统计 ,所得数据具有较好的重复性与稳定性 ,可以作为用于脉象信号识别的特征向量。In order to distinguish the pulse signals of the normal from the disease ones, two methods for extracting features are proposed based on the singularity theory and the multi-scale resolution character of the wavelet transform. They are continuous wavelet transform method and discrete wavelet transform method. According to the two methods, the scale-wave crest amplitude and the scale-energy are constructed. By processing and analyzing 235 pulse signals, the statistics show that the data have better repeatability and stability. So they can be regarded as the feature vectors to identify the pulse signals.

关 键 词：小波变换 脉象信号 特征提取 特征向量 

分 类 号：R318[医药卫生—生物医学工程] TN911.72[医药卫生—基础医学]