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Journal of Chinese Integrative Medicine ›› 2011, Vol. 9 ›› Issue (11): 1226-1233.doi: 10.3736/jcim20111111

• Original Clinical Research • Previous Articles     Next Articles

Study of traditional Chinese medicine pulse signals in patients with coronary heart disease based on recurrence quantification analysis

Rui Guo1, Yi-qin Wang1(), Jian-jun Yan2, Han-xia Yan1, Yu-ci Yang1   

  1. 1. Laboratory of Information Access and Synthesis of Traditional Chinese Medicine Four Diagnosis, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
    2. Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China
  • Received:2011-07-01 Accepted:2011-08-01 Online:2011-11-20 Published:2011-11-15

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Objective:By using recurrence quantification analysis (RQA) to analyze traditional Chinese medicine pulse signals of patients with coronary heart disease (CHD), this study aims to find nonlinear dynamic parameters of pulses to distinguish patients with CHD from normal subjects.
Methods: First, pulse signals were collected using ZBOX-Ⅰ pulse digitization gathering analyzer from October 2007 to June 2008. RQA was used to analyze RQA parameters of pulses of 63 patients with CHD and 61 normal subjects. RQA parameters included recurrence rate (RR), determinism (DET), averaged diagonal length (L), entropy of diagonal length (ENTR), length of longest diagonal line (Lmax), laminarity (LAM), trapping time (TT) and length of longest vertical line (Vmax). Then, rank-sum test and BoxPlot were employed to find significant difference and distribution of RQA parameters. Lastly, receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the measurements with significant difference.
Results: There were significant differences in RQA parameters of pulse signals between the two groups, including RR, DET, L, ENTR, LAM, TT and Vmax, and their areas under the ROC curves were 1.000, 0.898, 0.653, 0.673, 0.885, 0.898, 0.986 and 0.994, respectively.
Conclusion: Compared with the normal subjects, the pulse signals of the patients with CHD are presented with more certainty, regularity and stability. RQA measurements ofRR, TT, Vmax, DET and LAM show good diagnostic value according to their ROC curves.

Key words: coronary disease, pulse presentations, recurrence quantification analysis, sphygmogram, clinical trial

Figure 1

Recurrence plot of pulse signals a: An example of time series of pulse signal; b: Recurrence plot of time series present in a."

Figure 2

Analysis window (w=1 000)"

Table 1

RQA measures of the two groups (Mean rank)"


Group
RR
DET
L
Lmax
ENTR
LAM
TT
Vmax

Coronary heart disease
93.00*
86.79*
71.83*
62.78
73.06*
86.00*
92.16*
92.63*

Normal
31.00
37.41
52.87
62.21
51.59
38.23
31.87
31.38*

Figure 3

RQA parameters of the two groups 1: Coronary heart disease group; 2: Normal group. Boxplots display data distribution between the two groups. The middle black line of the box is the median, and range of the box is equal to difference between the third and first quartiles (IQR). The lower line is 1.5 IQR away from the first quartiles, and the upper line is 1.5 IQR away from the third quartiles. "o“ represents the outlier and "*” represents extreme points. RR: recurrence rate; DET: determinism; L: averaged diagonal length; ENTR: entropy of diagonal length; LAM: laminarity; TT: trapping time; Vmax: length of the longest vertical line."

Figure 4

ROC curve of RQA measures ROC: receiver operating characteristic; RQA: recurrence quantification analysis; RR: recurrence rate; DET: determinism; L: averaged diagonal length; ENTR: entropy of diagonal length; LAM: laminarity; TT: trapping time; Vmax: length of the longest vertical line."

Table 2

Area under the ROC curve"


Variable		 Area Standard deviation P value 95% Confidence interval
Below Upper

RR
1.000
0.000
0.000
1.000		 1.000

DET
0.898
0.029
0.000
0.841
0.955

L
0.653
0.050
0.003
0.556
0.750

ENTR
0.673
0.049
0.001
0.576
0.770

LAM
0.885
0.031
0.000
0.825
0.945

TT
0.986
0.010
0.000
0.966
1.007

Vmax
0.994
0.006
0.000
0.983
1.005
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