Doppler blood flow signal analysis meets traditional Chinese pulse diagnosis

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.