血栓多普勒信号的多参数提取及分类

血栓的准确检测可以用于早期脑血管疾病的诊断，超声多普勒是一种无损的血栓检测技术。文章使用三种信号处理方法：传统的声谱分析法、小波分析法、renyi信息量分析法对血栓多普勒信号进行分析，提取出相应的特征参数，然后对敏感的特征参数采用反向传输(Back-Propagation，简称BP)神经网络进行分类，建立起血栓、干扰噪声和正常血流信号的自动判别系统。通过对300例仿真多普勒信号和163例临床采集的大脑中动脉多普勒信号进行分析，结果表明：本文建立的系统对血栓的检测率高于传统的方法，有望可用于血栓多普勒信号的自动检测。

Extraction of multiple characteristics and classification of Doppler embolic signals

Embolic detection can be used for early diagnosis of cerebrovascular disease. The Doppler effect of ultrasound is a non-invasive means for the detection of emboli. In this paper, three signal processing methods, i.e., traditional spectral analysis, wavelet transform and Renyi information analysis, are used to analyze embolic Doppler signals. With the extracted characteristic parameters, a classification system using sensitive parameters is set up based on a BP neural network. The system can be used to classify emboli signals, interfering noise, and normal blood flow signals. From experiments of 300 simulated cases and 163 clinical cases of Doppler signals, it has been found that the embolic detection accuracy of the proposed method is higher than that using the traditional method. It is expected that automatic detection of emboli can be realized based on the proposed method.