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ABSTRACT: 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.
Evidence-based Complementary and Alternative Medicine 01/2013; 2013:602672. · 4.77 Impact Factor
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ABSTRACT: 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.
Journal of Traditional Chinese Medicine 12/2012; 32(4):571-7. · 0.30 Impact Factor
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ABSTRACT: The information about the nonstationarity of the auscultation signal is utilized in this paper to objectively and automatically
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).
Key wordsauscultation–nonstationarity–support vector machine (SVM)–traditional Chinese medicine (TCM)
Wuhan University Journal of Natural Sciences 04/2012; 16(1):83-87.
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ABSTRACT: Auscultation signals are nonstationary in nature. Wavelet packet transform (WPT) has currently become a very useful tool in analyzing nonstationary signals. Sample entropy (SampEn) has recently been proposed to act as a measurement for quantifying regularity and complexity of time series data. WPT and SampEn were combined in this paper to analyze auscultation signals in traditional Chinese medicine (TCM). SampEns for WPT coefficients were computed to quantify the signals from qi- and yin-deficient, as well as healthy, subjects. The complexity of the signal can be evaluated with this scheme in different time-frequency resolutions. First, the voice signals were decomposed into approximated and detailed WPT coefficients. Then, SampEn values for approximated and detailed coefficients were calculated. Finally, SampEn values with significant differences in the three kinds of samples were chosen as the feature parameters for the support vector machine to identify the three types of auscultation signals. The recognition accuracy rates were higher than 90%.
Evidence-based Complementary and Alternative Medicine 01/2012; 2012:247012. · 4.77 Impact Factor
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ABSTRACT: Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.
Evidence-based Complementary and Alternative Medicine 01/2012; 2012:135387. · 4.77 Impact Factor
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ABSTRACT: 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.
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 (L(max)), laminarity (LAM), trapping time (TT) and length of longest vertical line (V(max)). 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.
There were significant differences in RQA parameters of pulse signals between the two groups, including RR, DET, L, ENTR, LAM, TT and V(max), 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.
Compared with the normal subjects, the pulse signals of the patients with CHD are presented with more certainty, regularity and stability. RQA measurements of RR, TT, Vmax, DET and LAM show good diagnostic value according to their ROC curves.
Journal of Chinese Integrative Medicine 11/2011; 9(11):1226-33.
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ABSTRACT: Numerous researchers have taken the solid step forward towards the objectification research of Traditional Chinese Medicine (TCM) four diagnostic methods. However, it is deficient in studies on information fusion of the four diagnostic methods. We establish four-diagnosis syndrome differentiation model of TCM based on information fusion technology. The objective detection instruments of four-diagnostic method are applied to collect four-diagnosis objective information of 506 cases of clinical heart-system patients. Then multiple information fusion methods are adopted to establish recognition model of syndromes. The results of our experiments show that recognition rates of the six syndromes using multi-label learning is better than OCON artificial neural network and multiple support vector machine.
International Journal of Data Mining and Bioinformatics 01/2011; 5(4):369-82. · 0.43 Impact Factor
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IJDMB. 01/2011; 5:369-382.
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4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, Shanghai, China, October 15-17, 2011; 01/2011
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4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, Shanghai, China, October 15-17, 2011; 01/2011
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ABSTRACT: In this paper, factors contributing to the formation of pulse wave were analyzed based on hemodynamic principles. It is considered that formation of pulse wave was related to its propagation and reflection characteristics. Propagation of the pulse wave was characterized by pulse wave velocity, and reflection of the pulse wave was characterized by reflection coefficient. Pulse wave velocity and reflection coefficient were proposed as the eigenvectors of pulse wave in pulse diagnosis of traditional Chinese medicine, and support vector machine (SVM) was used to recognize slippery pulse, stringy pulse and plain pulse. Pulse wave velocity and reflection coefficient of the slippery, stringy and plain pulses in healthy people were calculated in this study, and SVM with Gaussian radial basis function was used for classifying. Results showed that pulse wave velocity and reflection coefficient with physiological and pathological significance had advantages in distinguishing slippery pulse, stringy pulse and plain pulse, which offered a new idea for recognizing pulse condition.
Journal of Chinese Integrative Medicine 08/2010; 8(8):742-6.
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ABSTRACT: This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.
International Journal of Bioinformatics Research and Applications 01/2010; 6(5):435-48.
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ABSTRACT: The traditional Chinese medicinal (TCM) inspection on tongue features are always observed with naked eye, and the syndrome is differentiated depended only on experience. These disadvantages cannot but badly impact not only the inheritance but also the advance and development of TCM. Hence, to bring out the standardization and objectivity of TCM tongue inspection is a task urgently expected. Along with the advancing of modernized TCM research, to study the tongue feature with modern scientific and technologic approaches for making it more quantitative, objective and standardized becomes the main research orientations of tongue inspection. Taking computerized automatic reorganization of tongue feature as a main clue, the techniques involving collection, resolution and signature analysis of tongue feature were discussed in this paper.
Zhongguo Zhong xi yi jie he za zhi Zhongguo Zhongxiyi jiehe zazhi = Chinese journal of integrated traditional and Western medicine / Zhongguo Zhong xi yi jie he xue hui, Zhongguo Zhong yi yan jiu yuan zhu ban 07/2009; 29(7):642-5.
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Guo-ping Liu,
Yi-qin Wang,
Ying Dong,
Nai-qing Zhao,
Zhao-xia Xu,
Fu-feng Li,
Hai-xia Yan,
Peng Qian, Rui Guo,
Xiao-dan Zhang,
Dan DI
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ABSTRACT: To develop an inquiry scale for diagnosis of heart system syndromes, and to discuss the provisional standardization of the inquiry method in traditional Chinese medicine (TCM).
Based on scale-making method, Chinese medicine theory and literature searching, an inquiry scale for diagnosis of heart system syndromes in TCM was developed. Statistics method, frequency counting and Delphi method were used for analysis. The inquiry scale was revised and tested repeatedly to check the test reliability, internal consistency reliability, and content validity, etc.
The inquiry scale for diagnosis of heart system syndromes mainly covered basic data, chief complaint, history of present illness (accompanying symptoms) and past history, with appendix of inspection and palpation information as well as diagnosis made according to traditional Chinese and Western medicine. Among them, general inquiries covered fever and chills, sweating, head-body and chest-belly symptoms, taste and diet, stool and urine, sleep, mood, and gynecologic symptoms, which were scaled in 8 dimensions. And 66 symptom variables were screened finally. The scale had a good content validity and its coefficient alpha was 0.82. For the results of test-retest reliability, the Kappa values of using the scale for diagnosis of heart-qi deficiency, heart-yang deficiency, turbid phlegm, and cold coagulation twice by the same doctor ranged from 0.74 to 1, showing that the consistency of the scale was relatively high. The Kappa values of evaluation of scorer reliability in diagnosis of heart-qi deficiency, heart-yang deficiency, and heart-yin deficiency were also high, which were 0.63, 0.72, 1 and 0.48 respectively. Other results of diagnosis had low-consistency or even no diagnostic agreement.
The research on the scale for inquiry in TCM indicates that it is feasible for the standardization of inquiry scale for diagnosis of heart system syndromes in TCM, offering a reference for research on the inquiry scales for other systems.
Journal of Chinese Integrative Medicine 02/2009; 7(1):20-4.
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I. J. Functional Informatics and Personalised Medicine. 01/2009; 2:325-340.
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2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, Washington, DC, USA, 1-4 November 2009, Proceedings; 01/2009
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ABSTRACT: A practical method is proposed to segment the wrist pulse waveform and estimate the average waveform. Some key issues that would affect the performance of the tasks are addressed. A zero-phase filtering was used to accommodate low frequency variations and high frequency noise without the phase-shift distortion, and a moving-window adaptive threshold based segmentation algorithm was used to ensure the segmenting performance. Waveform rotating and scaling, outlier elimination, cross-covariance based alignment, and average waveform estimation were introduced. Testing results show the effectiveness of segmentation performance, and the resulting average waveform well reflect the typical characteristics of the analyzed wrist pulse trend.
BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on; 06/2008
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Jianjun Yan,
Xiaojing Shen,
Chunming Xia,
Yong Shen,
Zhong Yan Gu,
Yiqin Wang,
Fufeng Li, Rui Guo,
Chunfeng Chen,
Lingyun Chen,
Bin Yan
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ABSTRACT: In this paper, wavelet packet energy entropy (WPEE) and support vector machine (SVM) were utilized to detect and classify auscultation signals in Traditional Chinese Medicine (TCM). The auscultation signals of health and qi-vacuity and yin-vacuity subjects were collected from the outpatient by Shanghai University of TCM. And the wavelet packet decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals, then to obtain energy entropies features of frequency bands. SVM are designed and trained for making a decision regarding the type of the auscultation signals. The experimental results showed the algorithm using WPEE and SVM classifier feasibility and effectiveness, and this paper is valuable for auscultation research in TCM.
Electrical and Control Engineering, International Conference on.
