Feature extraction and recognition for pulse waveform in Traditional Chinese Medicine based on hemodynamics principle

Abstract

Pulse diagnosis is one of important diagnosis methods in Traditional Chinese Medicine (TCM). Recognition of TCM pulse has received more and more attention in recent years. Extracting proper features is crucial for satisfactory classification. While most of previous methods for feature extraction of TCM pulse have no specific correlation with the mechanism of TCM pulse, a hemodynamics method is used to calculate the pulse waveform velocity (PWV) and pulse reflection factor(R), which reflects the principle of TCM pulse diagnosis. Then K-Nearest Neighbor (KNN) algorithm is employed to classify the data and double cross-validation method is used for accuracy assessment. An average accuracy rate of more than 97.8 % is achieved. It is concluded that the PWV and R may be used as the features for the classification of TCM pulses.