• Journal of Preventive Medicine and Public Health
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• v.39 no.6
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• pp.485-492
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• 2006

Objectives : To measure the distribution of electrocardiographic ST segment depression, and evaluate its relationships with cardiovascular risk factors based on the cross-sectional studies within a rural Korean community Methods : This study analyzed 1,343 persons, over 40 years old, who participated in a baseline survey during 2002-2005; the exclusion criteria included: a past history of myocardial infarction and angina pectoris, and specific conduction abnormalities. A Standard 12 leads ECG was recorded using an FCP-2101 (Fukuda Denshi Co.). The ST segment depression was retrospectively measured by a physician, according to the Minnesota code classification. Results : ST segment depression was found in 3.6 and 6.4% of male and female participants, respectively. After adjusting for age, gender, smoking, physical activity and obesity differences, high blood pressure showed significant relations with ST depression in females (male ORs=2.67, 95% CI=0.85-8.50; female ORs=2.62, 95% CI=1.29-5.32) Conclusions : As an ischemic ECG sign, ST depression was related to hypertension in female participants. This relationship remained significant, even after cases with left ventricular hypertrophy were removed.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1176-1194
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• 2018

The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.283-284
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• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.692-700
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• 2008

In some previous works on musical genre classification, human experts specify segments of a song for extracting musical features. Although this approach might contribute to performance enhancement, it requires manual intervention and thus can not be easily applied to new incoming songs. To extract musical features without the manual intervention, most of recent researches on music genre classification extract features from a pre-determined part of a song (for example, 30 seconds after initial 30 seconds), which may cause loss of accuracy. In this paper, in order to alleviate the accuracy problem, we propose a new method, which extracts features from representative segments (or main theme part) identified by structure analysis of music piece. The proposed method detects segments with repeated melody in a song and selects representative ones among them by considering their positions and energies. Experimental results show that the proposed method significantly improve the accuracy compared to the approach using a pre-determined part.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of Korean Neurosurgical Society
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• v.64 no.1
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• pp.69-77
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• 2021

Objective : Classification systems for cervical ossification of the posterior longitudinal ligament (OPLL) have traditionally focused on the morphological characteristics of ossification. Although the classification describes many clinical features associated with the shape of the ossification, including the concept of spondylosis seems necessary because of the similarity in age distribution. Methods : Patients diagnosed with OPLL who presented with increase signal intensity (ISI) on magnetic resonance imaging were surgically treated in our department. The patients were divided into two groups (pure versus degenerative) according to the presence of disc degeneration. Results : Of 141 patients enrolled in this study, more than half (61%) were classified into the degenerative group. The pure group showed a profound male predominance, early presentation of myelopathy, and a different predilection for ISI compared to the degenerative group. The mean canal compromise ratio (CC) of the ISI was 47% in the degenerative group versus 61% in the pure group (p<0.0000). On the contrary, the global and segment motions were significantly larger in the degenerative group (p<0.0000 and p=0.003, respectively). The canal diameters and global angles did not differ between groups. Conclusion : Classifying cervical OPLL based on the presence of combined disc degeneration is beneficial for understanding the disorder's behavior. CC appears to be the main factor in the development of myelopathy in the pure group, whereas additional dynamic factors appear to affect its development in the degenerative group.

• Health Policy and Management
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• v.12 no.2
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.757-772
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• 1993

This paper describes the knowledge extraction for image understanding in knowledge based system. The current set of low level processes operate on the numerical pixel arrays, to segment the image into region and to convert the image into directional image, and to calculate feature for these regions. The current set of intermedate level processes operate on the results of earlier knowledge source to build more complex representations of the data. We have grouped into thee categories : feature based classification, geometric token relation, perceptual organization and grouping.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.28-35
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• 2003

Quaternary faults found around the Ulsan Fault System can be divided into 4 types based on the fault outcrop features : Type I fault cuts basements and Quaternary deposits of which remain on both hangwall and footwall. Type II fault is developed only in Quaternary deposit. Type III fault has inclined unconformity after Quaternary faulting. Type IV fault is common type around the Ulsan fault system and has horizontal unconformity surface after cutting earlier Quaternary deposit. After erosion, later Quaternary deposit overlays on both old deposit and basement. The Ulsan Fault System consists of three segments at large scale from north to south based on the lineament rank and shape, Quaternary fault location, and slip rate. The segment boundaries are identified by the existence of the two intervals which show no lineaments and Quaternary faults. But, if detail fault parameters could be obtained and used in segmentation, it can be divided into more than three segments.

• The Journal of Korean Physical Therapy
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• v.15 no.1
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• pp.155-170
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• 2003

Lumbar segmental instability is considered to represent a significant sub-group within the chronic low back pain population. This condition has a unique clinical presentation that displays its symptoms and movement dysfunction within the neutral zone of the motion segment. The loosening of the motion segment secondary to injury and associated dysfunction of the local muscle system renders it biomechanically vulnerable in the neutral zone. There in evidence of muscle dysfunction related to the control of the movement system. There is a clear link between reduced proprioceptive input, altered slow motor unit recruitment and the development of chronic pain states. Dysfunction in the global and local muscle systems in presented to support the development of a system of classification of muscle function and development of dysfunction related to musculoskeletal pain. The global muscles control range of movement and alignment, and evidence of dysfunction is presented in terms of imbalance in recruitment and length between the global stability muscles and the global mobility muscles. The local stability muscles demonstrate evidence of failure of aeequate segmental control in terms of allowing excessive uncontrolled translation or specific loss of cross-sectional area at the site of pathology Motor recruitment deficits present as altered timing and patterns of recruitment. The evidence of local and global dysfunction allows the development of an integrated model of movement dysfunction. The clinical diagnosis of this chronic low back pain condition is based on the report of pain and the observation of movement dysfunction within the neutral zone and the associated finding of excessive intervertebral motion at the symptomatic level. Four different clinical patterns are described based on the directional nature of the injury and the manifestation of the patient's symptoms and motor dysfunction. A specific stabilizing exercise intervention based on a motor learning model in proposed and evidence for the efficacy of the approach provided.