-
[show abstract]
[hide abstract]
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.
-
[show abstract]
[hide abstract]
ABSTRACT: The ensemble empirical mode decomposition (EEMD) can be used to overcome the mode mixing problem of empirical mode decomposition (EMD) effectively. The EEMD method and Hilbert-Huang Transform (HHT) can be used to analyze pulse signals of Traditional Chinese Medicine (TCM). The amplitudes of the added white noise were about 0.1 and 0.2 time standard deviation of the investigated signal respectively. The difference of average frequency and average energy of every mode between normal pulse, slippery pulse, wiry pulse and wiry-slippery pulse were demonstrated based on different amplitudes of the added white noise. The results showed that it is more in line with clinical practice when the amplitude of the added white noise is about 0.2 time standard deviation of the investigated signal.
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 02/2011; 28(1):22-6.
-
[show abstract]
[hide abstract]
ABSTRACT: The lip color of a person is closely related to his or her health in the visual diagnosis of traditional Chinese medicine (TCM). The traditional method to judge the color of lips is through observing by a TCM doctor. The diagnosis result is affected not only by the doctor's knowledge and diagnosis experience, but also by the light, temperature and other environmental impacts. For these reasons, sometimes different doctors may make different judgement for the same lips. So it is urgently needed that an objective evaluation as reference for doctors can be obtained. A method based on support vector machine (SVM) that classifies lip color by computer automatically is presented in the present paper. Firstly, nine features of lip color in Hue, Saturation and Intensity (HSI) color space were extracted. Then, according to different combinations of these features five different experiments were conducted. By comparing the results of these experiments, it was discovered that the mean value is one of the most important features for the lip color. The overall effect of classification is better when the mean value and variance of HSI were chosen than other characteristics. In addition, experiments results demonstrated that the accuracy rate of classification is not improved when more features were adopted. The objective of the present paper is to select the appropriate characteristics and to combine them effectively to classify lip colors.
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 02/2011; 28(1):7-11.
-
IJDMB. 01/2011; 5:369-382.
-
2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, GA, USA, November 12-15, 2011; 01/2011
-
4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, Shanghai, China, October 15-17, 2011; 01/2011
-
[show abstract]
[hide abstract]
ABSTRACT: As the traditional diagnosis in traditional Chinese medicine, wrist pulses reflect the condition of the cardiovascular system which contains much nonlinearity. The linear analysis might not provide the enough useful information for the wrist pulse signals any more, so it is proposed the combination of wavelet packet and recurrence quantification plot analysis to analyze the wrist pulses in this paper. In this paper, 5 kinds of wrist pulse signals, which are the slippery pulses, the normal pulses, the thread pulses, the taut pulses and the slippery and taut pulses, are analyzed by using the recurrence quantification analysis which based on wavelet packets transform, and some results are obtained finally. From recurrence plot and recurrence quantification plot analysis based on wavelet packets transform, the differences can be observed among those 5 kinds of wrist pulses, which mean that the recurrence quantification plot analysis measures are the useful features to distinguish different kinds of traditional Chinese medicine (TCM) wrist pulses for the further classification.
Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on; 11/2010
-
[show abstract]
[hide abstract]
ABSTRACT: Chinese medical physicians obtain the patient's syndroms through inspection, auscultation and olfaction, inquiring, palpation(called si zhen in Chinese); and diagnose disease though 'syndrome differentiation'. Acquirement and integration of si zhen information are implemented according to personal knowledge and experience, which will lead to TCM diagnosis to be affected by subjective factors inevitably. Modern Information Technique is applied to TCM diagnosis, which has practical significance to help the objective of TCM diagnosis. This paper is aimed to illustrate the methods and status of objective research of TCM diagnosis. Firstly the objective, accurate and stable acquirement of si zhen information is demonstrated in this paper, then the practical and effective feature extraction and selection of si zhen information is discussed, lastly the pattern recognition and classification of syndrome in TCM diagnosis is presented.
Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on; 07/2010
-
[show abstract]
[hide abstract]
ABSTRACT: This study aims at utilizing mutual information algorithm to make an objective and quantitative research for the pulses in Traditional Chinese Medicine (TCM) diagnosis. The normal pulse signals, slippery pulse signals and taut pulse signals were collected from the outpatients by Shanghai University of TCM. Some typical pulse signals from all these classes were selected as the templates to be matched with the new pulse signals by means of mutual information, and then which class the new pulse signal should be classified was determined through the maximum mutual information. Finally, the satisfactory results were obtained which proof that it was appropriate to use mutual information to recognize pulse signals. The biggest advantage of this way is simple, since the inputted pulse signals should not be extracted features. And the time-domain features were extracted from the pulse signals to be classified. The results from the two different ways show the classification method presented is efficient based on Mutual Information.
Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on; 07/2010
-
Advances in Multimedia Information Processing - PCM 2010 - 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010, Proceedings, Part I; 01/2010
-
[show abstract]
[hide abstract]
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.
-
[show abstract]
[hide abstract]
ABSTRACT: Wrist pulse waveform analysis is common in traditional Chinese medicine (TCM) engineering and diagnosis modernization. Time-domain features extracted from wrist pulse wavefrom trains oscillate with respect to time. An application of spectral decomposition, i.e., spectral independent component analysis (ICA), to the variability analysis of these feature series is proposed in this paper. Time-relevant and magnitude-relevant feature series collections of controls and patients are analyzed by spectral ICA to extract dominant spectral components, which give the indications of the VLF (very low frequency), LF (low frequency), HF (high frequency) characteristics and relevant power contributions. The results from analyzing short-term real data show the feasibility of the proposed spectral decomposition method and indicate the potential of further applications to syndrome classification and diagnosis.
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on; 07/2009
-
[show abstract]
[hide abstract]
ABSTRACT: The cardiovascular system is complex system containing many nonlinearities. Pulse signals are nonlinear reflecting the status of the heart and the vascular system. Sample entropy analysis can quantify signal regularity or the system complexity generating the signal. In this paper the wrist pulse signals of healthy group and coronary heart disease group are analyzed and studied with sample entropy analysis, and the selection of parameters is discussed. The pulse signals of two groups are classified using support vector machine (SVM), the classification results are analyzed. The results indicate there was difference between the sample entropy of two groups of wrist pulse signals; SVM classifiers have good performance for classification of the two groups. Sample entropy analysis of wrist pulse was helpful for non-destructive inspection of coronary heart disease.
IT in Medicine and Education, 2008. ITME 2008. IEEE International Symposium on; 01/2009
-
I. J. Functional Informatics and Personalised Medicine. 01/2009; 2:325-340.
-
International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009, Shanghai, China, 3-5 August 2009; 01/2009
-
International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009, Shanghai, China, 3-5 August 2009; 01/2009
-
I. J. Functional Informatics and Personalised Medicine. 01/2009; 2:303-314.
-
2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, Washington, DC, USA, 1-4 November 2009, Proceedings; 01/2009
-
[show abstract]
[hide abstract]
ABSTRACT: Trachea is the unique passage for air to flow in and out. Its tone is of importance for the respiration system. However, investigation on how tracheal tone changes due to asthma is limited. Aiming at studying how the mechanical property changes due to asthma as well as the compliance and flow limitation, the following methods are adopted. Static and passive pressure-volume tests of rats' trachea of the asthmatic and control groups are carried out and a new type of tube law is formulated to fit the experimental data, based on which changes of compliance and limit flow rate are investigated. In order to give explanation to such changes, histological examinations with tracheal soft tissues are made. The results show that compliance, limit flow rate and material constants included in the tube law largely depend on the longitudinal stretching ratio. Compared with the control group, the tracheal compliance of asthmatic animals decreases significantly, which results in an increased limit flow rate. Histological studies indicate that asthma can lead to hyperplasia/hypertrophy of smooth muscle cells, and increase elastin and collagen fibres in the muscular membrane. Though decreasing compliance increases stability, during the onset of asthma, limit flow rate is much smaller due to the lower transmural pressure. Asthma leads to a stiffer trachea and the obtained results reveal some aspects relevant to asthma-induced tracheal remodelling.
Science in China Series C Life Sciences 11/2008; 51(10):922-31. · 1.61 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Auscultation is one of the Four Diagnosis Methods in traditional Chinese medicine (TCM). It has been researched through various methods, including digital techniques. This paper is aimed to review studies using digital signal processing techniques in recent years. Two kinds of methods (traditional methods and nonlinear methods) were illuminated briefly, including principles, main parameters and algorithms. According to the two methods, the studies were narrated and evaluated respectively, including tools, parameters, algorithms, aimed diseases and diagnostic effects. Researches on automatic stethoscope also been discussed, which research object was not speech but the sound of viscus. A new microcosmic technology - sonocytology and its application foreground in TCM was introduced simply, too. Finally, the conclusion was made. This survey provides a summary on the progress of digital processing techniques for auscultation in TCM, and will be useful for future researches in this area.
Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on; 08/2008
