The information about the nonstationarity of the aus-cultation signal is utilized in this paper to objectively and auto-matically identify healthy people and patients with qi-deficiency or yin-deficiency. In order to ...The information about the nonstationarity of the aus-cultation signal is utilized in this paper to objectively and auto-matically identify healthy people and patients with qi-deficiency or yin-deficiency. In order to characterize the nonstationarity of the sound signal,the nonlinear cross-prediction method is used to extract features from the signal. A feature selection method based on conditional mutual information maximization criterion （CMIM） is implemented to find an optimal feature set. By means of the support vector machine （SVM） classifier,three common states （healthy,qi-deficiency and yin-deficiency） in traditional Chinese medicine are distinguished using the feature set,and a satisfactory classification accuracy of 80% is achieved in the experiment. In conclusion,the analysis based on the nonstationarity of the sound signal provides an alternative and outstanding approach to the objective auscultation of traditional Chinese medicine （TCM）.展开更多
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 （...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.展开更多
Objective: To develop an effective Chinese Medicine(CM) diagnostic model of coronary heart disease(CHD) and to confirm the scientific validity of CM theoretical basis from an algorithmic viewpoint. Methods: Four types...Objective: To develop an effective Chinese Medicine(CM) diagnostic model of coronary heart disease(CHD) and to confirm the scientific validity of CM theoretical basis from an algorithmic viewpoint. Methods: Four types of objective diagnostic data were collected from 835 CHD patients by using a selfdeveloped CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm(REAL). Results: REAL was employed to establish a Xin(Heart) qi deficiency, Xin yang deficiency, Xin yin deficiency, blood stasis, and phlegm five-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. Conclusions: The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.展开更多
基金Supported by the National Natural Science Foundation of China (30701072)Supported by the National Science and Technology Support-ing Program in the Eleventh Five-Year Plan of China (2006BAI08B01-04)Construction Fund for Key Subjects of Shanghai (S30302)
文摘The information about the nonstationarity of the aus-cultation signal is utilized in this paper to objectively and auto-matically identify healthy people and patients with qi-deficiency or yin-deficiency. In order to characterize the nonstationarity of the sound signal,the nonlinear cross-prediction method is used to extract features from the signal. A feature selection method based on conditional mutual information maximization criterion （CMIM） is implemented to find an optimal feature set. By means of the support vector machine （SVM） classifier,three common states （healthy,qi-deficiency and yin-deficiency） in traditional Chinese medicine are distinguished using the feature set,and a satisfactory classification accuracy of 80% is achieved in the experiment. In conclusion,the analysis based on the nonstationarity of the sound signal provides an alternative and outstanding approach to the objective auscultation of traditional Chinese medicine （TCM）.
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)
文摘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.
基金Supported by the National Natural Science Foundation of China(No.81173199)Shanghai Sailing Program(No.15YF1412100)+2 种基金Young Teachers' Training Funded Project in Shanghai University(No.ZZszy13003)Budget for Research Shanghai Municipal Education Commission(No.2013JW06)China
文摘Objective: To develop an effective Chinese Medicine(CM) diagnostic model of coronary heart disease(CHD) and to confirm the scientific validity of CM theoretical basis from an algorithmic viewpoint. Methods: Four types of objective diagnostic data were collected from 835 CHD patients by using a selfdeveloped CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm(REAL). Results: REAL was employed to establish a Xin(Heart) qi deficiency, Xin yang deficiency, Xin yin deficiency, blood stasis, and phlegm five-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. Conclusions: The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.
