Objective: To compare the signals of pulse — diagnosis of healthy volunteer in different times in one day. Methods: After collecting the pulse waves of 42 healthy volunteers in 4 specific periods in one day, do pretreatment, parameter extracting basing on harmonic fitting, modeling, and identification by unsupervised learning and supervised learning with cross-validation step by step for analysis of the 4 groups. Finally, paired T-test and ANOVA were used in feature mining. Results: There are significant differences among the pulse-diagnosis signals of healthy volunteers in different times, and the accuracy rate is about 63%∼ 84%. Pulse rate, F1zuocun and F2zuocun, etc. are key features in classification. Conclusion: Signals of pulse-diagnosis in TCM of healthy human have circadian rhythmicity.

Objective: To compare the signals of pulse-diagnosis of 2 kinds of liver disease patients: Fatty Liver Disease (FLD) and Cirrhosis. Methods: After collecting the pulse waves of patients with Fatty Liver, Cirrhosis, we do pretreatment, parameter extracting basing on harmonic fitting, modeling, and identification by unsupervised learning and supervised learning with cross-validation step by step for analysis. Results: There is significant difference between the pulse-diagnosis signals of patients with FLD and Cirrhosis, and the result was confirmed by 3 analysis methods. The identification accurate is 72%–91%. Conclusion: Pulse waves collected from radial artery basing on the theory of TCM are specific for different pathological conditions. And the analysis methods we built in this study might offer some confidence for the realization of computer-aided diagnosis by pulse-diagnosis in TCM.

Traditional Chinese Pulse Diagnosis is a convenient and noninvasive method for disease diagnosis and healthcare. We have designed and implemented a Chinese wrist-pulse retrieval system based on the principle of Traditional Chinese Pulse Diagnosis (TCPD), called EasiCPRS. It is designed to be small in size, low in cost, with flexibility in deployment, and simplicity in operation. The contributions of this work are: 1. The wrist-pulse at "cun, guan and chi"points over the radial artery are obtained by applying a moderate and adjustable taking pressure during wrist-pulse retrieval. 2. A wrist-pulse signal conditioning circuit and a robust external taking pressure control algorithm are designed to overcome low signal-to-noise ratio (SNR). 3. A lightweight algorithm for wrist-pulse feature extraction is achieved on a resource-constrained platform to economize energy and bandwidth.