超声多普勒血流信号的小波特征提取及分类

利用小波变换对超声多普勒血流信号的最大频率曲线进行多尺度分析， 并从时间－尺度图上提取出模极大值的变化曲线。将这种方法应用到颈动脉血流的分析中，发现：该曲线对于脑血管床正常和异常的病例具有不同的形态。通过对该曲线进行多项式拟合，并将拟合的系数作为非线性变换单元组成的前馈网络（BP网络）的输入进行分类，临床试用效果良好，表明该方法为临床诊断脑血管疾病提供了一个新的依据。

Wavelet Feature Extraction and Classification of Doppler Ultrasound Blood Flow Signals

The maximum frequency waveforms of Doppler ultrasound blood flow signals were analyzed using a multi scale wavelet transform. The variation of maxima of wavelet transform modulus under various scales was extracted from the time scale representation. This novel approach was applied to the analysis of Doppler signals from carotid blood flow. It was found that the shape of this variation from cases with normal cerebral vessels differed from those associated with abnormal cases. The curve was fitted by a polynomial, and its coefficients were put into a back propagation (BP) neural network to make a classification. The clinical experiments showed that this approach got good performance and could be a new means in the clinical diagnosis of cerebral vascular disease.