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

Area of Science:

• Biomedical Engineering
• Traditional Chinese Medicine
• Data Science

Background:

• Pulse diagnosis (PD) is a traditional health assessment technique using radial artery palpation.
• The scientific validity of PD is questioned due to a lack of quantitative, objective evidence and understanding of pulse wave variations.
• Correlations between pulse wave characteristics and physiological states require further exploration.

Purpose of the Study:

• To develop a data-driven architecture for quantitative analysis of wrist pulse waves.
• To systematically quantify pulse wave characteristics and establish correlations with health status.
• To address skepticism surrounding PD by providing scientific evidence for its diagnostic capabilities.

Main Methods:

• Integrated a quantitative analysis method with medical knowledge and association establishment.
• Systematically quantified pulse waves, analyzing characteristics, typical waveforms, feature distributions, and recognition performance.
• Conducted experiments on 900 samples, utilizing feature visualization and classification.

Main Results:

• Demonstrated distinct differences in pulse waveforms across various diseases and health conditions.
• Identified and visualized variations in pulse features corresponding to different physiological states.
• Established correlations between quantitative pulse wave morphology and health status.

Conclusions:

• This study provides the first quantitative exploration into the diagnostic capability of wrist pulse evaluation.
• The findings enhance the credibility of traditional pulse diagnosis by linking waveform morphology to physiological changes.
• The data-driven approach offers a scientific basis for understanding and validating PD.