Feature extraction and recognition technique for human pulse signals

In Chinese traditional medicine, the pulse signal plays an important role for diagnosis. To investigate the effective technique for identifying heroin druggers through the pulse signal, the feature extraction algorithm based on the biocepstrum and the third-order cepstrum entropy of the pulse signal is studied. On the basis of concise and rigorous discussion for the algorithm, the biocepstrum-based diagonal slice components are estimated for human pulse signals of 20 heroin druggers and 20 healthy normal subjects. The magnitude of the sample value of diagonal slice at m=n=1 and the third-order cepstrum entropy of magnitudes of sample values of the diagonal slice within a particular region are used as two feature parameters for every human pulse signal to form a feature vector. A classifier based on the criterion of squared Mahalanobis distance is successfully designed. Applying the designed classifier to 40 feature vectors, the correct identification rate reaches 87.5%. The research result shows that the method of the feature extraction and classifier design presented in is valuable for identifying the human pulse signals of heroin druggers.

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