An automatic detection of Electrocardiogram (ECG) using Quantum Neural Networks (QNN) with multiresolution analysis is given in the paper. QNN originates from exploiting BP neural networks. With the quantum neurons, QNN can model the levels of uncertainty arising from complex classification problems. Its potential advantage over conventional methods is based on the argument for reliability. The fuzzy feature is expected to enhance the reliability of the network, which is critical for improving desirable diagnosis accuracy. And wavelet transform with multiresolution analysis is adopted in ECG pretreatment for reducing the numbers of neurons. So it can improve convergence speed of the network. The results of simulation confirmed the feasibility of the proposed approach.
Electrocardiography ; instrumentation ; methods ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated ; Quantum Theory ; Signal Processing, Computer-Assisted

The similarity measures between Vague sets are one of the most important technologies in Vague sets, In this paper, the new similarity measures based on Huang Guoshun's related works are presented and applied in electrocardiogram auto-recognition. Based on medical requiresments, in this paper, the characteristic parameters of signals from Massachusettes Institute of Technology (database) have been picked up and studied with BP neural network. In the end, the electrocardiogram samples are classified with the use of those characteristic parameters. The result shows that the accuracy of recognition goes up to 99.04%.
Algorithms ; Electrocardiography ; methods ; Fuzzy Logic ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted

Anxiety is usually generated because of the threatened feeling. The data of electrocardio, respiration, blood volume pulse and skin conductance signals were collected. The arithmetic of Relief were used for the feature selection and combined with k-Nearest Neighbor (kNN) arithmetic and Support Vector Machine (SVM) arithmetic for classification. The results show that the combination of Relief-SVM is better than combination of Relief-kNN on the recognition of anxiety state. The emotion recognition based on multi-physiological signals is superior to that based on one single signal.
Algorithms ; Anxiety ; Artificial Intelligence ; Humans ; Pattern Recognition, Automated ; Support Vector Machine

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.
Color ; Diagnosis, Differential ; Lip ; Medicine, Chinese Traditional ; methods ; Pattern Recognition, Automated ; methods ; Support Vector Machine

In order to correctly predict the malignant degree of brain glioma, three data mining algorithms: multi-layer perceptron network(MLP), decision tree, and rule induction are adopted to acquire diagnostic knowledge from patients with brain glioma cases. Totally 280 cases are collected, and some of them contain missing values. Preprocessing is taken to make them applicable to all three algorithms. Performance comparisons are carried out with a 10-fold cross validation test. Although the result of MLP is hard to be understood and cannot be applied directly, its reliability and accuracy are the highest when only a few hidden nodes are involved. Unlike MLP, both decision tree and rule induction use attribute-value pairs to represent diagnostic knowledge derived from treated cases. These could improve both the understandability and applicability of their results. When compared with rule induction, the inherent restriction in structure makes decision tree more efficient in decision-making but meanwhile hurts its simplicity, accuracy, and reliability. For testing samples, results of all these algorithms can achieve accuracy rate over 80%, which satisfies the basic requirement of neuroradiologists. If diagnostic accuracy rate is the main factor to be considered, MLP with only a few hidden nodes is the best. If the result is expected to be further checked or evaluated, rule induction will be the best algorithm. This work proves that data mining techniques can be used to obtain valid diagnostic knowledge from brain glioma cases and make computer aided diagnosis system in this field feasible.
Algorithms ; Brain Neoplasms ; diagnosis ; Decision Trees ; Diagnosis, Computer-Assisted ; methods ; Glioma ; diagnosis ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated

As is well known that the quality of training samples directly influence the recognizing ability of neural network. In this paper, we introduce a method for solving the problem of how to classify the pattern of forearm by obtaining typical samples. At first, the original samples were pretreated by using the membership class function that can improve the quality of cluster sample. Then, the center of clustering could be gained by using the method of clustering and the typical sample was obtained. Based on this method, we can get the typical sample that corresponds with the movements of stretch of arm and fold of arm. We can make them as the training sample of the BP network to classify the pattern of forearm. The experiment indicates that this measure can improve the point of identification.
Algorithms ; Cluster Analysis ; Electromyography ; methods ; Forearm ; physiology ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated ; methods ; Signal Processing, Computer-Assisted

This paper addresses the predicting problem of peritoneal fluid absorption rate(PFAR). An innovative predicting model was developed, which employed the improved genetic algorithm embedded in neural network for predicting the important PFAR index in the peritoneal dialysis treatment process of renal failure. The significance of PFAR and the complexity of dialysis process were analyzed. The improved genetic algorithm was used for defining the initial weight and bias of neural network, and then the neural network was used for finding out the optimal predicting model of PFAR. This method utilizes the global search capability of genetic algorithm and the local search advantage of neural network completely. For the purpose of showing the validity of the model, the improved optimal predicting model is compared with the standard hybrid method of genetic algorithm and neural network. The simulation results show that the predicting accuracy of the improved optimal neural network is greatly improved and the learning process needs less time.
Algorithms ; Artificial Intelligence ; Computer Simulation ; Forecasting ; Humans ; Neural Networks (Computer) ; Pattern Recognition, Automated ; methods ; Peritoneal Dialysis ; methods

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
Pattern Recognition, Automated ; Medical Informatics

Support Vector Machine (SVM) is an efficient novel method originated from the statistical learning theory. It is powerful in machine learning to solve problems with finite samples. Due to the deficiency of cancer cells, character of patient and noise in the raw data, it is very difficult to diagnose early cancer accurately. In this paper, SVM is employed in detecting early cancer and the results are encouraged compared with conventional methods. The accuracy of Non-linear SVM classifier is especially high in all kinds of classifiers, which indicates the potential application of SVM in early cancer detection.
Algorithms ; Artificial Intelligence ; Data Interpretation, Statistical ; Early Diagnosis ; Humans ; Models, Statistical ; Neoplasms ; diagnosis ; Neural Networks (Computer) ; Pattern Recognition, Automated

We have developed a hierarchical approach to define a set of FPGA configurations to solve the interconnect testing problem. This technique enables the detection, testing and verification of bridging faults involving intracluster interconnect and extracluster interconnect to be done easily.
Algorithms ; Artificial Intelligence ; Computers ; Equipment Design ; Equipment Failure Analysis ; Internet ; Neural Networks (Computer) ; Pattern Recognition, Automated ; methods ; Quality Control