• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.35-40
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• 2020

Recently, devices such as smart watches capable of measuring small biosignals have been released. Body composition, blood pressure, heart rate, and oxygen saturation can be easily obtained. However, the part that is not trusted by the user is accuracy. These biosignals are sensitive to the external environment and have large fluctuations depending on the conditions inside the subject's body. Blood pressure measurements, in particular, still give different results, depending on how the conditions in the body are handled. Therefore, in this study, PPG was analyzed to measure PTT at two points of 80% and 100%, the highest in PTT measurement. The effect of the measured value on SBP and DBP was analyzed and a method was proposed to increase the accuracy. As a result of the study, the measured value of PTT at 80% of the peak PPG is more effective in estimating blood pressure of SBP and DBP than the value measured at 100%. In the regression analysis of the rate of change blood pressure estimation, the coefficient of determination of SBP (80%) was 0.6946, and DBP (100%) was 0.547.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.1-10
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• 2020

In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1257-1264
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• 2019

The traditional cuff-based method for BP(Blood Pressure) measurement is not suitable for continuous real-time BP measurement techniques. For this reason, the previous studies estimated various blood pressures by fusion with the electrocardiography (ECG) and photoplethysmogram (PPG) sensor signals. However, conventional techniques based on PPG bio-sensing measurement face many challenging issues such as noisy supply fluctuation, small pulsation, and drifting non-pulsatile. This paper proposed a novel BP estimation methods using PPG and ECG sensors, which can be derived from the relationship between PPG and ECG using PTT(Pulse Transit Time) and PWV(Pulse Wave Velocity). Unlike conventional height ratio features, which are extracted on the basis of the peaks in the PPG and ECG waveform. The proposed method can be reliably obtained even if there are missing peaks among the sensed PPG signal. The increased reliability comes from periodical estimation of the peak-to-peak interval time using ECG and PPG. After 250,000 times trials of the blood pressure measurement, the proposed estimation technique was verified with the accuracy of ±28.5% error, compared to a commercialized BP device.

• The Korean Journal of Pharmacology
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• v.19 no.1
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• pp.123-131
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• 1983

1) To delineate the role of central ${\alpha}_2-adrenoceptors$ in the pressor response to raised intracranial pressure(ICP), the influence of yohimbine, an ${alpha}_2-adrenoceptor$ antagonist, on the pressor response to raised ICP was investigated in urethane-anesthetized rabbits. 2) The ICP was raised by infusing saline into a balloon placed in the epidural space. The rise of ICP was slow in the beginning of the infusion but it became sharp as the infusion proceeded. 3) In response to raised ICP, blood pressure(BP) tended to decrease slightly in the beginning and then increased sharply. BP, however, fell abruptly and markedly if ICP was raised further. The maximal pressor response to raised ICP was the increase of $49{\pm}2.4%$ of the original $BP(mean{\pm}SE\;in\;32\;experiments)$, and at this point the volume of saline infused into the balloon was $1.22{\pm}0.15\;ml$, and the ICP $165{\pm}6.4\;mmHg$. 4) Intraventricular yohimbine $(50{\mu}g)$ by itself did not affect BP. After the administration of this dose of yohimbine the increase of both ICP and BP was observed after the infusion of much smaller volume of saline than in the control animals, i.e., after the infusion of $0.83{\pm}0.02\;ml$ of saline the maximal increase of preesor response$(57{\pm}4.5%\;in\;6\;experiments)$ appeared and at this state the ICP was $164{\pm}9.6\;mmHg$. 5) Intraventricular $clonidine(30{\mu}g)$ markedly decreased BP by itself, and in the clonidine-treated rabbits the increase of ICP induced by the infusion was much less than in the control group and the pressor response to raised ICP was hardly seen. 6) The hypotensive effect of intraventricular clonidine was reversed by a susequent intraventricular $yohimbine(500\;{\mu}g)$. At this state the pressor response to raised ICP appeared as in the control animals. 7) These results show that the pressor response to raised ICP was facilitated when ${\alpha}_2-adrenoceptors$ in the rabbit brain was blocked by yohimbine and that yohimbine antagonized the inhibitory effect of clonidine on the pressor response to raised ICP.

• 대한핵의학회:학술대회논문집
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• 1987.05a
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• pp.121.2-121.2
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• 1987

• Monthly Duck's Village
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• s.67
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• pp.98-100
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• 2009

중국 최고의 미식가인 서태후가 미용식으로 즐겼다는 오리, 그만큼 비만예방과 고혈압 예방 및 치료에 탁월한 효과가 있는 다이어트 식품이라고 하는데...타 육류와 달리 불포화지방산의 함량이 높을 뿐 아니라 필수지방산인 리놀산과 아라키돈산이 함유되어 콜레스테롤 수치를 낮춰주는 역할을 해 스태미너 식품으로 꼽히는 오리. 또, 오리고기에는 독성물질을 분해하는 성분이 있어 유황을 먹인 오리는 해독복원의 으뜸으로 꼽힌다. 오리고기 속에 숨겨진 비밀을 알아본다.

• Journal of Life Science
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• v.22 no.2
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• pp.148-155
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• 2012

A cross-sectional analysis of blood pressure (BP) changes and the current prevalence and distribution of hypertension among age groups in Korea were conducted. Systolic (SBP), diastolic (DBP), and pulse (PP) pressures were evaluated. Residents visiting a local Community Health Center for a health check-up and/or participating in the Health Promotion Program were included for the analyses. A total of 6,570 subjects (2,809 men, 3,761 women) were divided into five age groups: 30-39, 40-49, 50-59, 60-69, and $70\leq$ years. Comparisons were made between sex and among age groups. SBP and DBP were continuously elevated, while men showed higher levels than women until their mid-60s; then, the trend was reversed. DBP in men became elevated from their 30s, reaching the highest in the 40s then slowly decreasing. DBP in women increased up to their 40s and 50s, then reached a plateau. PP increased rapidly from the 50s in both sexes. The major contributor of age-related BP elevation may be large artery stiffness in men, particularly from their 50s, while the blood pressure elevation of women may relate to diverse factors including large artery stiffness, stroke volume, and ventricular ejection rate. The rapid elevation of PP after the 50s in both men and women could represent a risk of cardiovascular or coronary mortality, particularly with increasing age. The current data showed a higher rate of hypertension in both sexes compared to previous reports. When those with both high-normal BP and hypertension are looked at in combination, BP management of the target population should be drastically elevated.

• Clinical and Experimental Pediatrics
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• v.50 no.3
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• pp.277-283
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• 2007

Purpose : Acute renal failure (ARF) is an important complication of rhabdomyolysis. The purpose of this study was to identify the major causes of rhabdomyolysis in children and to identify the factors associated with the developmet of ARF. Methods : A retrospective chart review between January 1997 to June 2005 was conducted of 60 patients with a diagnosis of rhabdomyolysis. Rhabdomyolysis was defined by an elevation of serum creatine phosphokinase (CK) greater than 1,000 IU/L with a MM fraction more than 95% or serum myoglobin>300 mg/dL or positive urine myoglobin. Patients were excluded if they had evidence of myocardial ischemia, or cerebrovascular insufficiency. Results : Sixty patients (37 males, 23 females) were enrolled, with the median age of 4.25 year. The most common causes of rhabdomyolysis were repiratory tract infection (9), seizure (7), hypoxia or asphyxia (6). Fifteen patients (25.0%) developed ARF and ten of them (66.0%) died. The initial serum creatinine, uric acid, potassium, pH and peak serum creatinine, initial systolic blood pressure, and mental status were statistically correlated with the development of ARF. The peak serum CK was associated with mortality of rhabdomyolysis. Conclusion : Acute renal failure was significant complication of rhabdomyolysis in children. Several clinical and laboratory factors were statistically associated with the development of ARF and death.

• The Korean Chronic Disease News
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• no.297
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• pp.1-18
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• 2005

국민보건은 우리에게 맡겨라/한국에‘세계줄기세포허브’개설/치매.중풍 노인들 간병 수발 문제 해결/조류독감 겁낼 필요없다/보건복지부 조직 전면 개편/임상노인의학회장에 유동준 회장 취임/희귀.난치성 질환 보험급여 대폭 확대/국립암센터, ‘세계최고’비전 제시/경기도립의료원, 서민병원으로 탈바꿈/감압치료 디스크탈출증에"탁월"/희망과 건강이 함께 하는 도시/현실성 있는 보건사업 전략 효율적 수행/고혈압의 예방 및 치료/‘금연’도 의학적 접근 필요하다/

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.