文献类型：期刊文献

中文题名：Recurrence quantification analysis on pulse morphological changes in patients with coronary heart disease

英文题名：Recurrence quantification analysis on pulse morphological changes in patients with coronary heart disease

作者：Rui Guo[1];Yiqin Wang[1];Jianjun Yan[2];Hanxia Yan[1]

机构：[1]Laboratory of Synthetic Study on TCM Diagnostic Information,Shanghai University of Traditional Chinese Medicine;[2]Center for Mechatronics Engineering,East China University of Science and Technology

年份：2012

卷号：32

期号：4

起止页码：571

中文期刊名：Journal of Traditional Chinese Medicine

外文期刊名：中医杂志（英文版）

收录：Scopus;CSCD:【CSCD2011_2012】；PubMed;

基金：Supported by Innovation Program of Shanghai Municipal Education Commission(No.11YZ71);the 3rd Shanghai Leading Academic Discipline Project(No.S30302);the National Natural Science Foundation of China(No. 81173199)

语种：英文

中文关键词：Pulse-taking; Cardiovascular diseases;Recurrence quantification analysis

外文关键词：脉冲信号;定量分析;心脏疾病;冠状动脉;形态;患者;非线性动态分析;心血管疾病

摘要：OBJECTIVE： To show that the pulse diagnosis used in Traditional Chinese Medicine, combined with nonlinear dynamic analysis, can help identify car- diovascular diseases. METHODS： Recurrence quantification analysis （RQA） was used to study pulse morphological changes in 37 inpatients with coronary heart dis- ease （CHD） and 37 normal subjects （controls）. An in- dependent sample t-test detected significant differ- ences in RQA measures of their pulses. A support vector machine （SVM） classified the groups accord- ing to their RQA measures. Classic time-domain pa- rameters were used for comparison. RESULTS： 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 distribu- tion 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〈0.0S）. The other measures （RR_std, L_std, Lmaxstd, 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-do- main （15 parameters） accuracy was 75.73%, and with time-domain （7 parameters） accuracy was 74.7O%. CONCLUSION： Nonlinear dynamic methods such as RQA can be used to study functional and struc- tural changes in the pulse noninvasively. Pulse sig- nals of individuals with CHD have greater regulari- ty, determinism, and stability than normal subjects, and their pulse morphology displays less variabili- ty. RQA can distinguish the CHD pulse from the healthy pulse with an accuracy of 88.21%, thereby providing an early diagnosis of cardiovascular dis- eases such as CHD.
OBJECTIVE:To show that the pulse diagnosis used in Traditional Chinese Medicine,combined with nonlinear dynamic analysis,can help identify cardiovascular diseases.METHODS: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.RESULTS: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%.CONCLUSION: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.