Baek Hwan Cho

Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia.

We proposed pattern classification based on principal components of cortical thickness between schizophrenic patients and healthy controls, which was trained using a leave-one-out cross-validation. The cortical thickness was measured by calculating the Euclidean distance between linked vertices on the inner and outer cortical surfaces. Principal component analysis was applied to each lobe for practical computational issues and stability of principal components. And, discriminative patterns derived at every vertex in the original feature space with respect to support vector machine were analyzed with definitive findings of brain abnormalities in schizophrenia for establishing practical confidence. It was simulated with 50 randomly selected validation set for the generalization and the average accuracy of classification was reported. This study showed that some principal components might be more useful than others for classification, but not necessarily matching the ordering of the variance amounts they explained. In particular, 40-70 principal components rearranged by a simple two-sample t-test which ranked the effectiveness of features were used for the best mean accuracy of simulated classification (frontal: (left(%)|right(%))=91.07|88.80, parietal: 91.40|91.53, temporal: 93.60|91.47, occipital: 88.80|91.60). And, discriminative power appeared more spatially diffused bilaterally in the several regions, especially precentral, postcentral, superior frontal and temporal, cingulate and parahippocampal gyri. Since our results of discriminative patterns derived from classifier were consistent with a previous morphological analysis of schizophrenia, it can be said that the cortical thickness is a reliable feature for pattern classification and the potential benefits of such diagnostic tools are enhanced by our finding.

Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods

OBJECTIVE Diabetic nephropathy is damage to the kidney caused by diabetes mellitus. It is a common complication and a leading cause of death in people with diabetes. However, the decline in kidney function varies considerably between patients and the determinants of diabetic nephropathy have not been clearly identified. Therefore, it is very difficult to predict the onset of diabetic nephropathy accurately with simple statistical approaches such as t-test or chi(2)-test. To accurately predict the onset of diabetic nephropathy, we applied various machine learning techniques to irregular and unbalanced diabetes dataset, such as support vector machine (SVM) classification and feature selection methods. Visualization of the risk factors was another important objective to give physicians intuitive information on each patients clinical pattern. METHODS AND MATERIALS We collected medical data from 292 patients with diabetes and performed preprocessing to extract 184 features from the irregular data. To predict the onset of diabetic nephropathy, we compared several classification methods such as logistic regression, SVM, and SVM with a cost sensitive learning method. We also applied several feature selection methods to remove redundant features and improve the classification performance. For risk factor analysis with SVM classifiers, we have developed a new visualization system which uses a nomogram approach. RESULTS Linear SVM classifiers combined with wrapper or embedded feature selection methods showed the best results. Among the 184 features, the classifiers selected the same 39 features and gave 0.969 of the area under the curve by receiver operating characteristics analysis. The visualization tool was able to present the effect of each feature on the decision via graphical output. CONCLUSIONS Our proposed method can predict the onset of diabetic nephropathy about 2-3 months before the actual diagnosis with high prediction performance from an irregular and unbalanced dataset, which statistical methods such as t-test and logistic regression could not achieve. Additionally, the visualization system provides physicians with intuitive information for risk factor analysis. Therefore, physicians can benefit from the automatic early warning of each patient and visualize risk factors, which facilitate planning of effective and proper treatment strategies.

Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kernels

Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their high accuracies. However, it is difficult to visualize the classifiers, and thus difficult to provide intuitive interpretation of results to physicians. We developed a new nonlinear kernel, the localized radial basis function (LRBF) kernel, and new visualization system visualization for risk factor analysis (VRIFA) that applies a nomogram and LRBF kernel to visualize the results of nonlinear SVMs and improve the interpretability of results while maintaining high prediction accuracy. Three representative medical datasets from the University of California, Irvine repository and Statlog dataset-breast cancer, diabetes, and heart disease datasets-were used to evaluate the system. The results showed that the classification performance of the LRBF is comparable with that of the RBF, and the LRBF is easy to visualize via a nomogram. Our study also showed that the LRBF kernel is less sensitive to noise features than the RBF kernel, whereas the LRBF kernel degrades the prediction accuracy more when important features are eliminated. We demonstrated the VRIFA system, which visualizes the results of linear and nonlinear SVMs with LRBF kernels, on the three datasets.

Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function

The heartbeat class detection of the electrocardiogram is important in cardiac disease diagnosis. For detecting morphological QRS complex, conventional detection algorithm have been designed to detect P, QRS, T wave. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. We applied two morphological feature extraction methods: higher-order statistics and Hermite basis functions. Moreover, we assumed that the QRS complexes of class N and S may have a morphological similarity, and those of class V and F may also have their own similarity. Therefore, we employed a hierarchical classification method using support vector machines, considering those similarities in the architecture. The results showed that our hierarchical classification method gives better performance than the conventional multiclass classification method. In addition, the Hermite basis functions gave more accurate results compared to the higher order statistics.

Continuous measurement of systolic blood pressure using the PTT and other parameters

In this paper, we proposed the regression model which could estimate unspecified peoples systolic blood pressure (SBP) conveniently and continuously and checked its accuracy through clinical experiments. The method for estimating each individual SBP by using only pulse transit time (PTT) has been studied, but it is difficult to estimate unspecified peoples SBP with the method using only PTT. Thus we researched several physical characteristic parameters which might affect blood pressure (BP) with the standard that we can measure them easily and conveniently, chose valid physical characteristic parameters through a clinical testing and correlation analysis, and made the regression model using PTT and valid physical characteristic parameters for estimating unspecified peoples SBP. Comparing the result of the proposed method with American National Standards Institute of the Association of the Advancement of Medical Instrument (ANSI/AAMI), we know that the proposed regression model gives an acceptable result

Movie-based VR therapy system for treatment of anthropophobia

The fear of public speaking is a kind of social phobia. The patients having the fear of public-speaking show some symptoms like shame and timidity in the daily personal relationship. They are afraid that the other person would be puzzled, feel insulted, and they also fear that they should be underestimated for their mistakes. For the treatment of the fear of public speaking, the cognitive-behavioral therapy is generally used. The cognitive-behavioral therapy is the method that makes the patients gradually experience some situations inducing the fears and overcome those at last. But if the real situations inducing fears cause dangerous symptoms or the patients have difficulty in imagining the situations, the effect of this method is notably reduced. And making the situations inducing the fears to patients requires a vast amount of effort and time. In this study, we developed the public-speaking simulator and the virtual environment for the treatment of the fear of public speaking. The head-mounted display, the head-tracker and the 3-dimensional sound system were used for immersing in the virtual environment. The virtual environment of this system is suggested in a seminar room where 6 virtual audiences are seated. The virtual audiences were made with real movies and inserted into the virtual environment. The patient speaks in front of these virtual audiences and the therapist can make virtual audience members respond with some motions. Moreover, clinical tests have been made to verify the effectiveness of the treatment.

VRIFA: a nonlinear SVM visualization tool using nomogram and localized radial basis function (LRBF) kernels

Prediction problems are prevalent in medical domains. For example, computer-aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs, especially SVMs with nonlinear kernels such RBF kernels, have shown superior accuracy in prediction problems. However, they are not favorably used by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized RBF (LRBF) kernel was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models. VRIFA graphically exposes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The tool has been used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases. VRIFA is accessible at http://dm.postech.ac.kr/vrifa .

The localization and visualization of breast lesion in digitized mammogram

Sometimes the location of breast lesion in mammogram could make radiologists confused. In this paper, we present a method for computerized localization of breast lesion and its visualization using Cartesian coordinates and computer graphics.

A New Approach for Personal Identification Based on dVCG

We propose a new approach to personal identification using derived vectorcardiogram (dVCG). The dVCG was calculated from recorded ECG using inverse Dower transform. Twenty-one features were extracted from the resulting dVCG. To analyze the effect of each feature and to improve efficiency while maintaining the performance, we performed feature selection using the Relief-F algorithm using these 21 features. Each set of the eight highest ranked features and all 21 features were used in SVM learning and in tests, respectively. The classification accuracy using the entire feature set was 99.53 %. However, using only the eight highest ranked features, the classification accuracy was 99.07 %, indicating only a 0.46 % decrease in accuracy compared with the accuracy achieved using the entire feature set. Using only the eight highest ranked features, the conventional ECG method resulted in a 93 % recognition rate, whereas our method achieved >99 % recognition rate, over 6 % higher than the conventional ECG method. Our experiments show that it is possible to perform a personal identification using only eight features extracted from the dVCG.

A study on the system for treatment of ADHD using virtual reality

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder characterized by a persistent pattern of inattention and/or hyperactivity/impulsivity that occurs in academic, occupational, or social settings. Though the number of men having this disorder increases gradually all over the world, the treatment for ADHD is limited to stimulant medications or a cognitive behavioral treatment. This fact caused us to develop the newly system for treatment of ADHD using Virtual Reality technology. Psychotherapy using VR has some advantages that it is safer and more effective than conventional therapeutic methods. We divided subjects into control group and VR group depending on whether they will have VR therapy with HMD & Tracking system. And we compared the results of CPT (Continuous Performance Test) between before and after the experiments. So we will show the effect of this VR system and the possibility VR technology can contribute greatly to the treatment of ADHD in this paper.