Recurrence quantification analysis on pulse morphological changes in patients with coronary heart disease

To show that the pulse diagnosis used in Traditional Chinese Medicine, combined with nonlinear dynamic analysis, can help identify cardiovascular diseases.

Recurrence quantification analysis (RQA) was used to study pulse morphological changes in 37 inpatients with coronary heart disease (CHD) and 37 normal subjects (controls). An independent sample t-test detected significant differences in RQA measures of their pulses. A support vector machine (SVM) classified the groups according to their RQA measures. Classic time-domain parameters were used for comparison.

RQA measures can be divided into two groups. One group of measures [ecurrence rate (RR), determinism (DEL), average diagonal line length (L), maximum length of diagonal structures (Lmax), Shannon entropy of the frequency distribution of diagonal line lengths (ENTR), laminarity (LAM), average length of vertical structures (TT), maximum length of vertical structures (Vmax)] showed significantly higher values for patients with CHD than for normal subjects (P < 0.05). The other measures (RR_std, L_std, Lmax_std, TT_std, Vmax_std) showed significantly lower values for the CHD group than for normal subjects (P < 0.05). SVM classification accuracy was higher with RQA measures: With RQA (16 parameters) accuracy was at 88.21%, and with RQA (12 parameters) accuracy was at 84.11%. In contrast, with classic time-domain (15 parameters) accuracy was 75.73%, and with time-domain (7 parameters) accuracy was 74.70%.

Nonlinear dynamic methods such as RQA can be used to study functional and structural changes in the pulse noninvasively. Pulse signals of individuals with CHD have greater regularity, determinism, and stability than normal subjects, and their pulse morphology displays less variability. RQA can distinguish the CHD pulse from the healthy pulse with an accuracy of 88.21%, thereby providing an early diagnosis of cardiovascular diseases such as CHD.