Data-Driven Study on Why Wrist Pulse Can Assess Health Conditions

Pulse diagnosis (PD) is a traditional diagnostic technique in which physicians palpate the wrist pulse at the radial artery to evaluate an individual's health status. However, the scientific validity of PD has been questioned due to the lack of quantitative and objective evidence. In particular, variations in wrist pulse waves across different health conditions remain underexplored, and the correlations between pulse wave characteristics and physiological states are not yet well understood. To fill these gaps, we design a data-driven analysis architecture that integrates a quantitative analysis method, medical knowledge, and association establishment. The quantitative analysis method systematically quantifies the pulse waves, presenting the pulse characteristics, typical pulse wave, feature distributions, and recognition performance. Experiments on 900 samples demonstrate the differences in pulse waveforms from various diseases. Further, feature visualization and classification present distinct variations in pulse across different health conditions. By integrating the quantitative results with medical knowledge, we establish the correlations between pulse waves and health status, interpreting how physiological changes affect pulse waveform morphology. This study is the first to quantitatively explore why wrist pulse can evaluate health conditions, thereby addressing longstanding skepticism surrounding PD. The findings of this paper significantly enhance PD's credibility.