• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.1
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• pp.76-85
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• 2010

In general, anesthetic depth is evaluated by experience of anesthesiologist based on the changes of blood pressure and pulse rate. So it is difficult to guarantee the accuracy in evaluation of anesthetic depth. The efforts to develop the objective index for evaluation of anesthetic depth were continued but there was few progression in this area. Heart rate variability provides much information of autonomic activity of cardiovascular system and almost all anesthetics depress the autonomic activity. Novel monitoring system which can simply and exactly analyze the autonomic activity of cardiovascular system will provide important information for evaluation of anesthetic depth. We investigated the anesthetic depth as following 7 stages. These are pre-anesthesia, induction, skin incision, before extubation, after extubation, Post-anesthesia. In this study, temporal, frequency and chaos analysis method were used to analyze the HRV time series from electrocardiogram signal. There were NN10-NN50, mean, SDNN and RMS parameter in the temporal method. In the frequency method, there are LF and HF and LF/HF ratio, 1/f noise, alphal and alpha2 of DFA analysis parameter. In the chaos analysis, there are CD, entropy and LPE. Chaos analysis method was valuable to estimate the anesthetic depth compared with temporal and frequency method. Because human body was involved the choastic character.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1777-1788
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• 2022

The reservoir is a major water supply source in the domestic agricultural environment, and the monitoring of water storage of reservoirs is important for the utilization and management of agricultural water resource. Remote sensing via satellite imagery can be an effective method for regular monitoring of widely distributed objects such as reservoirs, and in this study, image classification and image segmentation algorithms are applied to Sentinel-1 Synthetic Aperture Radar (SAR) imagery for water body detection in 53 reservoirs in South Korea. Six algorithms are used: Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), and Chan-Vese (CV), and the results of water body detection are evaluated with in-situ images taken by drones. The correlations between the in-situ water surface area and detected water surface area from each algorithm are NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, and CV 0.9736, and the larger the scale of reservoir, the higher the linear correlation was. WS showed low recall due to the undetected water bodies, and NN, SVM, and RF showed low precision due to over-detection. For water body detection through SAR imagery, we found that aquatic plants and artificial structures can be the error factors causing undetection of water body.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1247-1251
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• 2016

This study aims to investigate the effect of sampling frequency to the time domain and frequency domain analysis of pulse rate variability (PRV). Typical time domain variables - AVNN, SDNN, SDSD, RMSSD, NN50 count and pNN50 - and frequency domain variables - VLF, LF, HF, LF/HF, Total Power, nLF and nHF - were derived from 7 down-sampled (250 Hz, 100 Hz, 50 Hz, 25 Hz, 20 Hz, 15 Hz, 10 Hz) PRVs and compared with the result of heart rate variability of 10 kHz-sampled electrocardiogram. Result showed that every variable of time domain analysis of PRV was significant at 25 Hz or higher sampling frequency. Also, in frequency domain analysis, every variable of PRV was significant at 15 Hz or higher sampling frequency.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1370-1378
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• 2018

There is a problem to measure neutral bio-signals during sleep because of inconvenience of attaching lots of sensors. In this study, we measured single electrocardiogram(ECG) signal and analyzed the correlation with sleep. After R-peak detection from ECG signal, we extracted 9 features from time and frequency domain of heart rate variability(HRV). Mean of HRV, RR intervals differing more than 50ms(NN50), and divided by the total number of all RR intervals(pNN50) have significant differences in each sleep stage. Specially, the mean HRV has an average of 87.8% accuracy in classifying sleep and awake status. In the future, the measurement ECG signal minimizes inconvenience of attaching sensors during sleep. Also, it can be substituted for the standard sleep measurement method.

• Environmental Mutagens and Carcinogens
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• v.21 no.2
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• pp.149-155
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• 2001

Eighty-one workers including 38 employees directly incinerating industry wastes were recruited from a company located in South Korea. To evaluate the association between urinary 1-hydroxypyrene glucuronide (1-OHPG) levels, as internal dose of polycyclic aromatic hydrocarbon (PAH) exposure, and glycophorin A (GPA) mutation frequency, as an early biologic effect indicator. Urinary 1-OHPG levels were measured by synchronous fluorescence spectroscopy after immunoaffinity purification using monoclonal antibody 8E11. Erythrocyte GPA variant frequency (NN or NO) was assessed in MN heterozygotes with a flow cytometic assay. The GSTM1 and GSTT1 genotypes were assessed by a multiplex PCR method. The GPA NN phenotype frequency was higher in occupationally exposed group (n=14, mean$\pm$S.D. 6.6$\pm$12.0 in 10/SUP 6/ erythrocyte cells) than in non-exposed group (n=22, 2.1$\pm$3.5). Similarly, the GPA(NO or NN) phenotype frequency was higher in exposed group (n=14, 9.7$\pm$17.3) than non-exposed group (n=22, 4.2$\pm$6.3). The above differences failed to reach statistical significance, but a significant increase was seen in GPA variant frequency levels with increase in urinary 1-OHPG levels (Spearman's correlation: p=0.06 (NO), p=0.07 (NO or NN)). When this association was evaluated by GSTM1 genotype status, the association between GPA mutation and urinary 1-OHPG levels was stronger in individuals with GSTM1 present genotype (Spearmans correlation; r=0.50, p=0.02). These results suggest that the association between urinary 1-OHPG and GPA mutation is be modulated by the GSTM1 genotype.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3925-3932
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• 2013

Emotion is closely related to the life of human, so has effect on many parts such as concentration, learning ability, etc. and makes to have different behavior patterns. The purpose of this paper is to extract important features based on physiological signals to recognize negative emotion. In this paper, after acquisition of electrocardiography(ECG), electroencephalography(EEG), skin temperature(SKT) and galvanic skin response(GSR) measurements based on physiological signals, we designed an accurate and fast algorithm using combination of linear discriminant analysis(LDA) and genetic algorithm(GA), then we selected important features. As a result, the accuracy of the algorithm is up to 96.4% and selected features are Mean, root mean square successive difference(RMSSD), NN intervals differing more than 50ms(NN50) of heart rate variability(HRV), ${\sigma}$ and ${\alpha}$ frequency power of EEG from frontal region, ${\alpha}$, ${\beta}$, and ${\gamma}$ frequency power of EEG from central region, and mean and standard deviation of SKT. Therefore, the features play an important role to recognize negative emotion.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.387-392
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• 2018

The aim of this study was to determine the statistical significance of heart rate variability(HRV) between sleep stages, Apnea-hypopnea index(AHI) and age in patients with obstructive sleep apnea(OSA). This study evaluated the main parameters of HRV over time domain and frequency domain in 40 patients with sleep apnea. The non-REM(sleep stage) was statistically validated by comparing the AHI degree of the three groups(mild, moderate, severe) of sleep apnea patients. The NN50(p=0.043), pNN50(p=0.044), VLF peak(p=0.022), LF/HF(p=0.028) were statistically significant in the R-R interval of patients with sleep apnea from the control group (p<0.05). The LF / HF (p = 0.045) and HF power (p = 0.0395) parameters between the non-RAM sleep (sleep 2 phase) and REM sleep in patients with sleep apnea were statistically significant in the control group(p<0.05). We may be able to provide a basis for understanding the correlation among AHI, sleep stage and age and heart rate variability in patients with obstructive sleep apnea.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1509-1518
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• 2017

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

• Journal of Biomedical Engineering Research
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• v.35 no.4
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• pp.75-80
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• 2014

This paper proposed a method that can automatically classify sleep apnea by using features extracted from pulse rate variability(PRV) signals induced from piezo snoring sensor for patients with obstructive sleep apnea(OSA). We have extracted eight features(NN, SDNN, RMSSD, NN10, NN50, LF, HF and LF/HF ratio) based on time and frequency analyses of PRV. Sleep apnea was classified by a linear discriminant analysis(LDA). A performance was evaluated using snore recordings from 13 patients with OSA (ages: $54.5{\pm}10.5$ years, body mass index: $26.3{\pm}2.5kg/m^2$, apnea-hypopnea index: $19.2{\pm}6.0/h$). The sensitivity and specificity were $78.9{\pm}0.9%$ and $78.9{\pm}0.9%$ for training set and $77.7{\pm}10.9%$ and $79.0{\pm}2.8%$ for test set, respectively. Our study demonstrated the feasibility of implementing a piezo snoring sensor based on a portable device as a simple and cost-effective solution for contributing to the OSA screening.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5007-5010
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• 2013

Heat shock proteins are molecular chaperones that may be constitutively present in cells protecting them from various stresses, such as extreme temperature, anoxia or chemical agents. Cervical cancer is the second most prevalent malignancy of women. In this study, we analyzed the expression of Hsp27 by immunohistochemistry in cervical intraepithelial lesions of Brazilian women, along with samples from non neoplasic lesions (NN). Cervical intraepithelial neoplasia I (CIN I), II (CIN II) and III (CIN III)/in situ carcinoma and squamous cell carcinoma (SCC) were included. Immunostaining was observed in 30 (100%) samples of NN, 46 (92%) in CIN I, 50 (100%) in CIN II, 52 (98.11%) in CIN III/CIS, and 46 (98.11%) in SCC. In group NN Hsp27 immunostaining was heterogeneous, more intense in basal and parabasal layers of the epithelium and less or absent in the intermediate and superficial layer. The majority of the samples of CIS and SCC presented strong staining in all epithelial layers. Metaplasic cells, when present, were strongly stained. In this study, Hsp27 protein was found to be commonly expressed in cervical epithelial cells.