Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label.

OBJECTIVE: To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific 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 self-developed 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 defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-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.

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine.

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

Nonlinear Analysis of Auscultation Signals in TCM Using the Combination of Wavelet Packet Transform and Sample Entropy.

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%.

Study on intelligent syndrome differentiation in traditional Chinese medicine based on multiple information fusion methods.

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.

[Analysis of infinity optical thickness between nine kinds of composite resins with the same color mark].

OBJECTIVE: This study compared the infinity optical thickness of composite resins with the same color marks. METHODS: In this test nine composite resins (shade A2) were used. A plastic mold (10mm in diameter, 4mm in height) was used to prepare 27 disk specimens. Color was measured by CIE L*a*b* relative to CIE source against a white and a black background, using a colorimeter. Color difference (DeltaE*) was calculated as DeltaE=[(DeltaL*(2)+(Deltaa*)(2)+(Deltab*)(2)](1/2). The heights of composite resins were recorded as DeltaE is approximately 1.5. The infinity optical thickness of composite resins when DeltaE = 1.5 was calculated by regression formulation. RESULTS: The results indicated that composite resins with hybrid filler and opaque pigments were superior infinity optical thickness. Composite resins contain micro-filler were inferior infinity optical thickness. CONCLUSION: Although composite resins with the same color mark had the similar fillers, they had different color and infinity optical thickness.

[Study on the infinity optical thickness of light-cured composite resins and attached shade guides].

OBJECTIVE: This study compared the opaque characteristics of light-cured composite resins and attached shade guides. METHODS: Three kinds of light-cured composite resins and attached shads guides were used. A plastic mold (10mm in diameter and 4mm in height) was used to prepare disk specimens. Color was measured by CIE L*a*b* relative to CIE source against a white and a black background, using a colorimeter. Color change (DeltaE)was calculated as DeltaE=[(DeltaL*)(2)+(Deltaa*)(2)+(Deltab*)(2)](1/2). The heights of composite resins were recorded as DeltaE is approximately 1.5. The infinity optical thickness of composite resins when delta E*=1.5 was calculated by regression formulation. RESULTS: There was significant difference in color difference and the infinity optical thickness among materials and shades. The infinity optical thickness gradual decreased in the same shade mark from 1 to 4, and from A to D the results was not simple. CONCLUSION: There was significant difference in the infinity optical thickness between the attached shade guides and the resins materials. Composite resins contain micro-filler were inferior in infinity optical thickness.