摘要
海洛因吸毒者与正常人脉象信号最显著的区别在于作为时间函数的幅度波形。两者在某特定的时间区域内的幅值及其变化速率均呈现显著的差别。然而,由小波变换所得的脉象信号尺度系数的子分量和小波系数的子分量恰好可以揭示这样的关键特征。应用小波变换的多分辨率分析法对15例海洛因吸毒者和15例正常人的脉象信号进行分析,利用db2正交小波对每一例脉象信号进行3层分解,取出第3层尺度系数的第6个分量和第3层小波系数的第2个分量的绝对值构成特征向量。设计具有良好性能的概率神经网络对获得的30个特征向量进行自动检测。在网络的设计中,取20个特征向量作为训练样本,另外10个作为测试样本。据此,15例正常人和15例吸毒者全部予以正确地检测出来,检测率达到了100%。
The most significant difference between the human pulse signals collected from heroin druggers and healthy persons is at their amplitude waveforms as time functions. That is, the amplitude values and change rates of two types of signals, within a particular time range, appear different features. However, the partial components of the scaling and wavelet coefficients of the pulse signals obtained by using wavelet transform can reveal such key features. The pulse signals of 15 heroin druggers and 15 healthy persons are analyzed through using the muhiresolution analysis of wavelet transform. By using db2 orthogonal wavelet, every pulse signal is decomposed into three levels and the absolute values of the sixth component of scaling coefficients and the second component of the wavelet coefficients in the third level are combined to form a feature vector. A probabilistic neural network with good detection performance is successfully designed for automatically detecting 30 feature vectors. During the network design, 20 feature vectors are used as training samples. The remained 10 feature vectors are used as testing samples. Based on these steps, 15 heroin druggers and 15 healthy persons are all correctly identified. In other words, the detection rate arrives at 100%. druggers.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第10期50-54,共5页
Journal of Chongqing University
基金
重庆市自然科学基金资助项目(CST2004BB)
关键词
小波变换
多分辨率分析
尺度系数
小波系数
概率神经网络
海洛因吸毒者
脉象信号
wavelet transform
muhiresolution analysis
scaling coefficient
wavelet coefficient
probabilistic neural network
heroin druggers
pulse signal
