The gas discharge visualization (GDV) order parameter model based on the principle of mastering both permanence and change

Abstract

Objective

To investigate the human body’s complex system, and classify and characterize the human body’s health states with “a comprehensive integrated method from qualitative to quantitative”.

Methods

This paper introduces the concept of “order parameters” and proposes a method for establishing an order parameter model of gas discharge visualization (GDV) based on the principle of “mastering both permanence and change (MBPC)”. The method involved the following three steps. First, average luminous intensity ($\overline{I}$) and average area ($\overline{S}$) of the GDV images were calculated to construct the phase space, and the score of the health questionnaire was calculated as the health deviation index (H). Second, the k-means++ clustering method was employed to identify subclasses with the same health characteristics based on the data samples, and to statistically determine the symptom-specific frequencies of the subclasses. Third, the distance (d)<italic/> between each sample and the “ideal health state”, which determined in the phase space of each subclass, was calculated as an order parameter describing the health imbalance, and a linear mapping was established between the d and the H. Further, the health implications of GDV signals were explored by analyzing subclass symptom profiles. We also compare the mean square error (MSE) with classification methods based on age, gender, and body mass index (BMI) indices to verify that the phase space possesses the ability to portray the health status of the human body.

Results

This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants. Based on the discovered linear law, the current model can use d calculated by measuring the GDV signal to predict H (R² > 0.77). Combined with the symptom profiles of the subclasses, we explain the classification basis of the phase space based on the pattern identification. Compared with common classification methods based on age, gender, BMI, etc., the MSE of phase space-based classification was reduced by an order of magnitude.

Conclusion

In this study, the GDV order parameter model based on MBPC can identify subclasses and characterize individual health levels, and explore the TCM health meanings of the GDV signals by using subjective-objective methods, which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.