ABSTRACT
Idiopathic thrombocytopenic purpura (ITP) is a common bloody disease with a high incidence in children, but its diagnostic method is exclusive diagnosis, and the existing detection techniques are mostly invasive, which may cause secondary injury to patients and also may increase the risk of disease. In order to make up for the lack of the detection method, this study made a preliminary exploration on the diagnosis of children's ITP from the perspective of infrared thermography. In this study, a total of 11 healthy children and 22 ITP children's frontal infrared thermal images were collected, and the pattern characteristic (PFD), average temperature (Troi) and maximum temperature (MAX) characteristics of 7 target areas were extracted. The weighted PFD parameters were correlated with the platelet count commonly used in clinical diagnosis, and the sensitivity and specificity of the weighted PFD parameters for children's ITP were calculated through the receiver operating characteristic curve (ROC). The final results showed that the difference of the weighted PFD parameters between healthy children and ITP children was statistically significant, and the parameters negatively correlated with platelet count. Under the ROC curve, the area under the curve (AUC) of this parameter is as high as 92.1%. Based on the research results of this paper, infrared thermography can clearly show the difference between ITP children and healthy children. It is hoped that the methods proposed in this paper can non-invasively and objectively describe the characteristics of ITP infrared thermal imaging of children, and provide a new ideas for ITP diagnosis.
Subject(s)
Child , Humans , Area Under Curve , Platelet Count , Purpura, Thrombocytopenic, Idiopathic , ThermographyABSTRACT
Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.
Subject(s)
Algorithms , MicroRNAs , Chemistry , Support Vector MachineABSTRACT
This study was aimed to discover the knowledge of urination formula-syndrome in theTreatise on Exogenous Febrile Diseasebased on the Sunshine diagram of multi-layer complex concept network express. A total of 39 items about urination formula-syndrome in theTreatise on Exogenous Febrile Diseasewere collected, and then regulated into standard expression. The database was established and the multi-layer complex concept network express was constructed. The Sunshine diagram was drawn and the connotation rules on urination formula-syndrome in theTreatise on Exogenous Febrile Diseasewere summarized through mode development of the diagram. The results showed that the Sunshine diagram collected 44 objects (i.e., formulas) and 191 properties (i.e. syndromes), which expressed the urination formula-syndrome visually. It was concluded that the application of Sunshine diagram in the formula-syndrome knowledge based on multi-layer complex concept network express provided certain references on the inheritance and development of classics in traditional Chinese medicine (TCM).
ABSTRACT
Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.
Subject(s)
Humans , Algorithms , Electroencephalography , Entropy , Epilepsy , Diagnosis , Multivariate Analysis , Nonlinear DynamicsABSTRACT
This study was aimed to analyze the regulation of syndrome-treatment pattern of classical Chinese medici-nal formulae for emotional diseases based on formal concept analysis. First, we dealt with the decision formal context of 51 prescriptions about emotional symptom in the Treatise on Febrile and Miscellaneous Diseases and the Es-sentials from the Golden Cabinet based on the principle of optimization. Then, we generated a new partial-order at-tribute diagram in order to present the specific character. Finally, we explained properties of partial-order structure graph from traditional Chinese medicine (TCM) experts' point of view based on knowledge discovery. The results indi-cated the relationship between prescription and syndrome of emotional diseases. It was concluded that method pro-posed in this paper worked well in treatment of description of syndrome differentiation and discovery of new knowl-edge from the known data in the clinical diagnosis.
ABSTRACT
Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCI I, BCI II_IV and USPS. The classification rate were 97%, 82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0. 2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.
Subject(s)
Humans , Algorithms , Brain-Computer Interfaces , Electroencephalography , ClassificationABSTRACT
To increase efficiency of automated leucocyte pattern recognition using lower feature dimensions, a novel inter-class distinctive feature selection method for chromatic leucocyte images was proposed based on attribute hierarchical relationship. According to the attribute constraints in formal concept analysis, we established a knowledge representation and discovery method based on the hierarchical optimal diagram by defining attribute value and visual representation of optimized hierarchical relationship. It was applied to human peripheral blood leucocytes classification and 12 distinctive attributes were simplified from 60 inter-class attributes, which contributes significantly to reduced feature dimensions and efficient inter-class feature classification. Compared with the classical experimental data, the inter-class distinctive feature selection method based on hierarchical optimal diagram was proved to be usable and effective for six leucocyte pattern recognition.
Subject(s)
Humans , Leukocytes , Classification , Pattern Recognition, AutomatedABSTRACT
The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.
Subject(s)
Female , Humans , Algorithms , Artificial Intelligence , Breast Neoplasms , Classification , Diagnosis , Discriminant AnalysisABSTRACT
On the basis of the theory of formal concept analysis (FCA), a new method for generation of an attribute hierarchical graph is proposed in this paper. This method can solve the problems of how to mine and express classification knowledge and rules in compatibility of prescription. In this paper, we view prescriptions as objects that possess certain attributes of the named drugs. First, the formal context is established based on theory. Then optimization of the original formal context and extracts the connotation and extension of the concept are followed, constructing attribute hierarchical graph. Finally, useful knowledge from the hierarchical diagram of attributes based on the way of knowledge representation is mined. The result showed that the method for discovering Traditional Chinese Prescription (TCP) diagnostic knowledge is feasible and effectual for small samples. The research of large samples is 13th open question of FCA. It is an international subject to be studied urgently.
Subject(s)
Humans , Concept Formation , Drug Combinations , Drug Prescriptions , Reference Standards , Drugs, Chinese Herbal , Chemistry , Therapeutic Uses , Medicine, Chinese Traditional , Reference StandardsABSTRACT
To solve the ineffective problem of leukocytes classification based on multi-feature fusion in a single color space, we proposed an automatic leukocyte pattern recognition by means of feature fusion with color histogram and texture granular in multi-color space. The interactive performance of three color spaces (RGB, HSV and Lab), two features (color histogram and texture granular) and four similarity measured distance metrics (normalized intersection, Euclidean distance, chi2-metric distance and Mahalanobis distance) were discussed. The optimized classification modes of high precision, extensive universality and low cost to different leukocyte types were obtained respectively, and then the recognition system of tree-integration of the optimized modes was established. The experimental results proved that the performance of the fusion classification was improved by 12.3% at least.
Subject(s)
Humans , Algorithms , Clinical Laboratory Techniques , Color , Image Enhancement , Methods , Image Interpretation, Computer-Assisted , Methods , Leukocyte Count , Methods , Leukocytes , Classification , Cell Biology , Pattern Recognition, Automated , MethodsABSTRACT
Facial paralysis is a frequently-occurring disease, which causes the loss of the voluntary muscles on one side of the face due to the damages the facial nerve and results in an inability to close the eye and leads to dropping of the angle of the mouth. There have been few objective methods to quantitatively diagnose it and assess this disease for clinically treating the patients so far. The skin temperature distribution of a healthy human body exhibits a contralateral symmetry. Facial paralysis usually causes an alteration of the temperature distribution of body with the disease. This paper presents the use of the histogram distance of bilateral local binary pattern (LBP) in the facial infrared thermography to measure the asymmetry degree of facial temperature distribution for objective assessing the severity of facial paralysis. Using this new method, we performed a controlled trial to assess the facial nerve function of the healthy subjects and the patients with Bell's palsy respectively. The results showed that the mean sensitivity and specificity of this method are 0.86 and 0.89 respectively. The correlation coefficient between the asymmetry degree of facial temperature distribution and the severity of facial paralysis is an average of 0.657. Therefore, the histogram distance of local binary pattern in the facial infrared thermography is an efficient clinical indicator with respect to the diagnosis and assessment of facial paralysis.
Subject(s)
Humans , Facial Paralysis , Diagnosis , Infrared Rays , Pattern Recognition, Automated , Methods , Skin Temperature , ThermographyABSTRACT
Chromatography of fingerprint as an important tool has been used in identification and quality control of herbal medicines, and it is gaining more and more attention. Among the various methods, chromatography gradually becomes the mainstream for its characteristics. This paper describes the techniques of chromatography of fingerprint including pretreatments for sample data set, the establishment of chromatographic fingerprint and fingerprint visualization. It emphasizes several analysis methods and their scope of application. Visualization technology combined with fingerprint makes analysis more intuitive. Finally, existing key problems and future works were also discussed.
Subject(s)
Chromatography , Methods , Drugs, Chinese Herbal , Chemistry , Gas Chromatography-Mass Spectrometry , Methods , Quality Control , Spectrum Analysis , Methods , X-Ray DiffractionABSTRACT
Epilepsy is a common chronic neurological disease, which is caused by excessive brain neuron discharge. The epileptic seizure has the characteristic of abruptness and reiteration. Prediction of seizures has great significance for patients to take timely and effective clinical measures. The symbolic dynamics method was introduced to analyze absence epilepsy EEG. The key parameters affecting the symbolic statistical quantities were discussed. The symbolic entropy and time irreversebility were calculated in different epilepsy stages. It was found that the symbolic entropy and the time irreversebility were rather big in interictal stage. The two parameters declined significantly during the transformation process from interictal stage to ictal stage and maintained lower value during ictal stage. The results showed that the symbolic dynamics method could reflect the changes of epilepsy EEG. The symbolic entropy and time irreversebility are sensitive features indicating different stages of seizures and have potential important clinical applications.
Subject(s)
Animals , Male , Rats , Algorithms , Brain , Electroencephalography , Epilepsy , Rats, Inbred Strains , Signal Processing, Computer-AssistedABSTRACT
Electrical defibrillation is the most effective way to treat the ventricular tachycardia (VT) and ventricular fibrillation (VF). An automatic external defibrillator based on DSP is introduced in this paper. The whole design consists of the signal collection module, the microprocessor controlingl module, the display module, the defibrillation module and the automatic recognition algorithm for VF and non VF, etc. This automatic external defibrillator has achieved goals such as ECG signal real-time acquisition, ECG wave synchronous display, data delivering to U disk and automatic defibrillate when shockable rhythm appears, etc.
Subject(s)
Humans , Algorithms , Defibrillators , Equipment Design , Tachycardia, Ventricular , Therapeutics , Ventricular Fibrillation , TherapeuticsABSTRACT
The vector space transformations such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA) or the kernel-based methods may be applied on the extracted feature from the field, which could improve the classification performance. A barycentre graphical feature extraction method of the star plot was proposed in the present study based on the graphical representation of multi-dimensional data. The feature order question of the graphical representation methods affecting the star plot was investigated and the feature order method was proposed based on the improved genetic algorithm (GA). For some biomedical datasets, such as breast cancer and diabetes, the obtained classification error of barycentre graphical feature of star plot in the GA based optimal feature order is very promising compared to the previously reported classification methods, and is superior to that of traditional feature extraction method.
Subject(s)
Algorithms , Artificial Intelligence , Biomedical Research , Computer Graphics , Data Collection , Discriminant Analysis , Linear Models , Pattern Recognition, Automated , Methods , Principal Component AnalysisABSTRACT
To solve the problem of cardiac arrhythmias classification, we proposed a novel algorithm based on the multi-feature fusion and support vector machines (SVM). Kernel independent component analysis (KICA) was used to extract nonlinear features and wavelet transform (WT) was used to extract time-frequency features. Combining these features could include more information about the disease. We designed the classification model based on SVM combined with error correcting output codes (ECOC). Receiver operating characteristic curve (ROC) and Area Under the ROC curve (AUC) value were used to assess the classification model. The value of AUC is 0.956 against MIT-BIH arrhythmia database. Experimental results showed effectiveness of the proposed method.
Subject(s)
Humans , Algorithms , Area Under Curve , Arrhythmias, Cardiac , Classification , Diagnosis , Electrocardiography , Methods , Principal Component Analysis , ROC Curve , Signal Processing, Computer-Assisted , Support Vector MachineABSTRACT
This article introduces some commonly used methods of ozone concentration detection, including chemical method, UV absorption method, and electrochemical method etc., introduces the latest four ozone concentration sensors, and summarizes the advantages and disadvantages of each method. In addition, the article emphatically introduces the ozone's applications and development in the medical aspects. Prospects for the use of ozone concentration detection, ozone treatment and ozone therapy instrument are also demonstrated in it. The literature collected and reviewed on ozone concentration detection and ozone therapy includes 37 papers in English, and 50 papers in Chinese, but only 30 articles among them are included in this review (19 in Chinese and 11 in English), according to the principle of eliminating the old information and repetitive contents. The present paper selects only those on ozone, ozone concentration, ozone therapy and ozone therapy instrument.
Subject(s)
Humans , Hepatitis , Drug Therapy , Mouth Diseases , Drug Therapy , Ozone , Therapeutic UsesABSTRACT
The characteristics of electrocardiogram (ECG) signal are fuzzy and random, so that they are difficult for automatic analysis and diagnosis. To solve this problem, an uncertainties transformation model-Cloud Model, which is a fusion of qualitative and quantitative information, was tried to use to analyze the ECG signal. The model fusions the characters of fuzzy and random, just suit to the ECG automatic analysis and diagnosis system. Based on the theory of the cloudy transform and comprehensive cloud, the clustering of ECG signal was finished. Further more, the clinic experience of expert was summarized as classification rules based on the theory. The experiment data were from MIT/ BIH database. The experiment results showed more close to those of the expert's analysis. The describing result was more close to those of the more expert's with qualitative and quantitative information. It is well concluded that the method is an effective ECG signal analysis method.
Subject(s)
Humans , Algorithms , Electrocardiography , Methods , Fuzzy Logic , Models, Theoretical , Signal Processing, Computer-AssistedABSTRACT
Objective To study a novel feature extraction method of Chinese materia medica (CMM) fingerprint. Methods On the basis of the radar graphical presentation theory of multivariate, the radar map was used to figure the non-map parameters of the CMM fingerprint, then to extract the map features and to propose the feature fusion. Results Better performance was achieved when using this method to test data. Conclusion This shows that the feature extraction based on radar chart presentation can mine the valuable features that facilitate the identification of Chinese medicine.
ABSTRACT
With the development of economy, the incidence of diabetes is keeping on rising. It has been a larger chief offender endangering human health. Glucose monitoring in time, accurately and continuously can provide the basis for the adjustment of diet, exercise and drug treatment project, and can control disease at the level of satisfaction degree. Noninvasive measurement of glucose avoids blood collection with high frequency, alleviates pain caused by blood sampling, and prevents infection. It comes with hope for the diabetic. In this article, we compare the kinds of techniques, introduce the theory, the problems of polarization rotation, the solving methods and the advantages, thus providing references for the noninvasive measurement of glucose.