• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.294-298
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• 2020

We was constructed of the spread movement with tremor layer point by the tractile-dot structure that was analyzed the squirm quake forms of the perception movement on the atom liquid. Algorithm of squirm quake forms was used to move the spread tremor on the atom state. To detect the tiny signal, we compared the association average value of the squirm quake form on the atom state. Their subject were issued the valuation standard and perception movement for basic atom condition by the spread tremor. We take to detect the tiny scores of average during perception movement side from the spread tremor that magnetic condition get to a variation of the Ma-αAVG and Ma-αMAX-MIN with 6.25±0.35 units, that electric condition get to a variation for the El-αAVG and El-αMAX-MIN with 5.68±0.42 units. The spread tremor was to investigate the capacity of the tremor form, to uptake a spread data of spread tremor level on the CCPL that was denoted the calm-classification form by the spread perception level system. As the squirm quake forms was demanded by the spread tremor signal, max-average values of perception movement were checked the spread position for association average data. We make mention of squirm quake forms for a signal association and a quake data signal of relation system.

• 대한의생명과학회지
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• 제10권4호
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• pp.485-493
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• 2004

In the current field of Medical Informatics, the information increases, and changes fast, so we can access the various data types which are ranged from text to image type. A small number of technician digitizes these data to establish database, but it is needed a lot of money and time. Therefore digitization by many end-users confronting data and establishment of searching database is needed to manage increasing information effectively. New data and information are taken fast to provide the quality of care, diagnosis which is the basic work in the medicine. And also It is needed the medical database for purpose of private study and novice education, which is tool to make various data become knowledge. However, current medical database is used and developed only for the purpose of hospital work management. In this study, using text input, file import and object images are digitized to establish database by people who are worked at the medicine field but can not expertise to program. Data are hierarchically constructed and then knowledge is established using a tree type database establishment method. Consequently, we can get data fast and exactly through search, apply it to study as subject-oriented classification, apply it to diagnosis as time-depended reflection of data, and apply it to education and precaution through function of publishing questions and reusability of data.

• 대한의용생체공학회:의공학회지
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• 제10권3호
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• pp.343-350
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• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.443-446
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• 1997

HRV(heart rate variability) is the time series data of R-R interval time duration based on ECGs. Power spectral analysis of HRV has recently been used to define the activity of ANS(autonomic nervous system). In this study, 14 rats were divided into two groups, sympathectomy and vagotomy. During the experiments, ECGs of rats were collected three times at each experimental conditions or the duration of 5 minutes, where sampling frequency was set at 2KHz. After the application of the Berger's Serires algorithm to ECG raw data, power spectrum of HRV was obtained via FFT. Results showed that HF/LF were increased or the sympathectomy group and decreased or the vagotomy group. It implies that the variations in HF/LF components could be used or the ANS function classification.

• 대한의용생체공학회:의공학회지
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• 제40권2호
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• pp.48-54
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• 2019

This paper proposed a method of validation data augmentation for improving the grading accuracy of diabetic macular edema (DME) using deep learning. The data augmentation technique is basically applied in order to secure diversity of data by transforming one image to several images through random translation, rotation, scaling and reflection in preparation of input data of the deep neural network (DNN). In this paper, we apply this technique in the validation process of the trained DNN, and improve the grading accuracy by combining the classification results of the augmented images. To verify the effectiveness, 1,200 retinal images of Messidor dataset was divided into training and validation data at the ratio 7:3. By applying random augmentation to 359 validation data, $1.61{\pm}0.55%$ accuracy improvement was achieved in the case of six times augmentation (N=6). This simple method has shown that the accuracy can be improved in the N range from 2 to 6 with the correlation coefficient of 0.5667. Therefore, it is expected to help improve the diagnostic accuracy of DME with the grading information provided by the proposed DNN.

• 대한의생명과학회지
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• 제24권3호
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• pp.221-229
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• 2018

Trichophyton rubrum is one of the well-known pathogenic fungi and causes dermatophytosis and cutaneous mycosis in human world widely. However, there are not an available sequence type (ST) classification methods and previous studies for T. rubrum until now. Therefore, currently, molecular biological tools using their DNA sequences are used for genotype identification and classification. In the present study, in order to characterize the genetic diversity and the phylogenetic relation of T. rubrum clinical isolates, five different housekeeping genes, such as actin (ACT), calmodulin (CAL), RNA polymerase II (RPB2), superoxide dismutase 2 (SOD2), and ${\beta}$-tubulin (BT2) were analyzed using by multilocus sequence typing (MLST). Also, DNA sequence analysis was performed to examine the differences between the sequences of Trichophyton strains and the identified genetic variations sequence. As a result, most of the sequences were shown to have highly matched rates in their housekeeping genes. However, genetic variations were found on three different positions of ${\beta}$-tubulin gene and were shown to have changed from $C{\rightarrow}G$ (1766), $G{\rightarrow}T$ (1876), and $C{\rightarrow}A$ (1886). To confirm the association with T. rubrum inheritance, a phylogenetic tree analysis was performed. It was classified as four clusters, but there was little significant correlation. Even so, MLST analysis is believed to be helpful for determining the genetic variations of T. rubrum in cases where there is more large-scale data accumulation. In conclusion, the present study demonstrated the first MLST analysis of T. rubrum in Korea and explored the possibility that MLST could be a useful tool for studying the epidemiology and evolution of T. rubrum through further studies.

• 대한의용생체공학회:의공학회지
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• 제43권5호
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• pp.361-368
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• 2022

The purpose of this study was to develop a hand rehabilitation training system for hemiplegic patients. We also tried to find out five hand postures (WF: Wrist Flexion, WE: Wrist Extension, BG: Ball Grip, HG: Hook Grip, RE: Rest) in real-time using multi-channel EMG-based deep learning. We performed a pre-processing method that converts to Spider Chart image data for the classification of hand movement from five test subjects (total 1,500 data sets) using Convolution Neural Networks (CNN) deep learning with an 8-channel armband. As a result of this study, the recognition accuracy was 92% for WF, 94% for WE, 76% for BG, 82% for HG, and 88% for RE. Also, ten physical therapists participated for the usability evaluation. The questionnaire consisted of 7 items of acceptance, interest, and satisfaction, and the mean and standard deviation were calculated by dividing each into a 5-point scale. As a result, high scores were obtained in immersion and interest in game (4.6±0.43), convenience of the device (4.9±0.30), and satisfaction after treatment (4.1±0.48). On the other hand, Conformity of intention for treatment (3.90±0.49) was relatively low. This is thought to be because the game play may be difficult depending on the degree of spasticity of the hemiplegic patient, and compensation may occur in patient with weakened target muscles. Therefore, it is necessary to develop a rehabilitation program suitable for the degree of disability of the patient.

• 전기학회논문지
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• 제65권7호
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• pp.1236-1241
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• 2016

Cancer has been the most frequent in Korea, and pathogenesis and progression of cancer have been known to be occurred through various causes and stages. Recently, the research of chromosomal and genetic disorder and the research about prognostic factor to predict occurrence, recurrence and progress of chromosomal and genetic disorder have been performed actively. In this paper, we analyzed DNA methylation data downloaded from TCGA (The Cancer Genome Atlas), open database, to research bladder cancer which is the most frequent among urinary system cancers. Using three level of methylation data which had the most preprocessing, 59 candidate CpG island were extracted from 480,000 CpG island, and then we analyzed extracted CpG island applying data mining technique. As a result, cg12840719 CpG island were analyzed significant, and in Cox's regression we can find the CpG island with high relative risk in comparison with other CpG island. Shown in the result of classification analysis, the CpG island which have high correlation with bladder cancer are cg03146993, cg07323648, cg12840719, cg14676825 and classification accuracy is about 76%. Also we found out that positive predictive value, the probability which predicts cancer in case of cancer was 72.4%. Through the verification of candidate CpG island from the result, we can utilize this method for diagnosing and treating cancer.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.637-644
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• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• Genomics & Informatics
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• 제14권2호
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• pp.46-52
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• 2016

Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.