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首页> 外文会议>2010 3rd International Conference on Biomedical Engineering and Informatics >Doppler blood flow signal analysis meets traditional Chinese pulse diagnosis
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Doppler blood flow signal analysis meets traditional Chinese pulse diagnosis

机译：多普勒血流信号分析符合中国传统脉搏诊断

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In traditional Chinese pulse diagnosis (TCPD), diseases of internal organs can be detected by recognizing pulse waveform patterns of wrist radial arterial. However pulse waveform analysis, for which Doppler diagnosis is a powerful tool, is limited to cardiovascular diseases. This paper tries to fill the gap between TCPD and Doppler diagnosis by applying signal analysis and pattern recognition technologies to Doppler blood flow signals (DBFS's) of wrist radial arterial, which are recorded from both hands of healthy people, gastritis and cholecystitis patients. DBFS's are classified using the features proposed by an L2-soft margin support vector machine (L2-SVM): five clinical Doppler parameters (DP), wavelet energies (WE), wavelet packet energies (WPE), and piecewise axially integrated bispectra (PAIB). 5-fold cross validation is used for performance evaluation. The sick are differentiated from the healthy with an accuracy of about 80% using DP, WE and WPE, while the classification rate between gastritis and cholecystitis reaches 100%. Using PAIB, ether two groups of subjects are classified with accuracy greater than 93%. Gastritis is more accurately recognized than cholecystitis, while the latter is recognized with a higher accuracy on data from the left hand than right. Though the sample size is relatively small, we still argue that the methods proposed here are effective and could serve as an assisstive tool for TCPD.
机译：在中国传统的脉搏诊断（TCPD）中，可以通过识别腕radial动脉的脉搏波形来检测内部器官的疾病。然而，多普勒诊断是强有力的工具，脉搏波形分析仅限于心血管疾病。本文试图通过将信号分析和模式识别技术应用于腕radial动脉的多普勒血流信号（DBFS）来填补TCPD和多普勒诊断之间的空白，这些信号是从健康人，胃炎和胆囊炎患者的双手记录下来的。使用L2-soft边缘支持向量机（L2-SVM）提出的功能对DBFS进行分类：五个临床多普勒参数（DP），小波能量（WE），小波包能量（WPE）和分段轴向积分双谱（PAIB） ）。 5倍交叉验证用于性能评估。使用DP，WE和WPE可以将病人与健康人区分开，准确率约为80％，而胃炎和胆囊炎之间的分类率达到100％。使用PAIB，对两组对象进行分类的准确度大于93％。胆囊炎比胆囊炎更准确地被识别，而胆囊炎从左手得到的数据比从右得到的数据更准确。尽管样本量相对较小，但我们仍然认为此处提出的方法是有效的，并且可以用作TCPD的辅助工具。

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