Research on Zheng Classification Fusing Pulse Parameters in Coronary Heart Disease

Abstract EN

This study was conducted to illustrate that nonlinear dynamic variables of Traditional Chinese Medicine (TCM) pulse can improve the performances of TCM Zheng classification models.

Pulse recordings of 334 coronary heart disease (CHD) patients and 117 normal subjects were collected in this study.

Recurrence quantification analysis (RQA) was employed to acquire nonlinear dynamic variables of pulse.

TCM Zheng models in CHD were constructed, and predictions using a novel multilabel learning algorithm based on different datasets were carried out.

Datasets were designed as follows: dataset1, TCM inquiry information including inspection information; dataset2, time-domain variables of pulse and dataset1; dataset3, RQA variables of pulse and dataset1; and dataset4, major principal components of RQA variables and dataset1.

The performances of the different models for Zheng differentiation were compared.

The model for Zheng differentiation based on RQA variables integrated with inquiry information had the best performance, whereas that based only on inquiry had the worst performance.

Meanwhile, the model based on time-domain variables of pulse integrated with inquiry fell between the above two.

This result showed that RQA variables of pulse can be used to construct models of TCM Zheng and improve the performance of Zheng differentiation models.