• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1720-1724
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• 2012

This study describes an oscillometric-based blood pressure measuring algorithm by detecting turning points of oscillation signal from digitally filtered cuff signals of an automatic sphygmomanometer. The blood pressure measuring algorithm uses a characteristic ratios method from the turning points. The accurate values of the systolic/diastolic blood presures(SBP/DBP) are calculated using the peaks in the ranges of characteristic ratios. Performances of the proposed algorithm and four automatic sphygmomanometers are compared with the mercury manometer(manual type sphygmomanometer), regarding the SBP and DBP values of manual sphygmomanometer as the reference values. The performance test showed the proposed algorithm revealed the best results in errors and a statistical analysis. Therefore this algorithm can be usable in any automatic sphygmomanometers.ssure states. This may be compromising results for subject-independent sensibility evaluation using EEG signal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.451-454
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• 2000

Invasive methode and Non-invasive methode are used in blood pressure measurement. The Invasive methode can Set the correct measured blood pressure but, it has patient feels uncomfortable. So most of cases use Non-invasive methode. The Oscillometric method is commonly apply to modem electric sphygmomanometer and using various algorithm. In this paper describe about a algorithm it control and to determinate the cuff pressure, and filtering that data for measure the blood pressure. The communicating with personal computer can pressure deflation is by Solenoid valve and it uses RS-232 system in packet communication. The main using algorithm for blood pressure measurements are maximum amplitude algorithm and oscillometric algorithm. MAA(maximum amplitude algorithm) has various measured oscillation it depend on patient's age, height, weight and arm circumference size. In this paper, 1 studied the various measured oscillation apply to characteristic ratio and can get the result of systolic blood pressure, diastolic blood pressure, mean blood pressure. It was not used same ratio to measuring oscillation. In the MAA(maximum amplitude algorithm), we hope for reduce the difference with the real blood pressure and the measured blood pressure, when it applied with various specific ratio.

• Journal of Sensor Science and Technology
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• v.17 no.2
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• pp.87-94
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• 2008

Blood pressure (BP), one of the most important vital signs, is used to identify an emergency state and reflects the blood flow characteristics of the cardiovascular system. The conventional noninvasive method of measuring BP is inconvenient because patients must wear a cuff on their arm and the measurement process takes time. This paper proposes an algorithm for estimating the BP using the pulse transit time (PTT) of the photoplethysmography (PPG) and pressure pulse from finger at the same time as a more convenient way to measure the BP. After recording the electrocardiogram (ECG), measuring the pressure pulse, and performing PPG, we calculated the PTT from the acquired signals. Then, we used a multiple regression analysis to measure the systolic and diastolic BP indirectly. Comparing the BP measured indirectly using the proposed algorithm and the real BP measured with a sphygmomanometer, the systolic pressure had a mean error of ${\pm}3.240$ mmHg and a standard deviation of 2.530 mmHg, while the diastolic pressure had a satisfactory result, i.e., a mean error of ${\pm}1.807$ mmHg and a standard deviation of 1.396 mmHg. These results are more superior than existing method estimating blood pressure using the one PTT and satisfy the ANSI/AAMI regulations for certifying a sphygmomanometer i.e., the measurement error should be within a mean error of ${\pm}5$ mmHg and a standard deviation of 8 mmHg. These results suggest the possibility of applying our method to a portable, long-term BP monitoring system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.493-498
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• 2006

In this paper, we propose a blood pressure measurement method using ANFIS. Usually, the maximum and minimum blood pressures are calculated by Maximum Amplitude Algorithm(MAA) method. However, the MAA method has some drawbacks to measure exact blood pressure since it uses a fixed ratio to set the measuring points for everyone without considering individual's special conditions. To solve this problem, the pressures measured by the MMA are trained by ANFIS having self-learning ability. From various experiments, we confirm that the proposed method shows better performance than conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1209-1223
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• 2022

Blood pressure is one of the key physiological parameters for determining human health, and can prove whether human cardiovascular function is healthy or not. In general, what we call blood pressure refers to arterial blood pressure. Blood pressure fluctuates greatly and, due to the influence of various factors, even varies with each heartbeat. Therefore, achievement of continuous blood pressure measurement is particularly important for more accurate diagnosis. It is difficult to achieve long-term continuous blood pressure monitoring with traditional measurement methods due to the continuous wear of measuring instruments. On the other hand, radar technology is not easily affected by environmental factors and is capable of strong penetration. In this study, by using machine learning, tried to develop a linear blood pressure prediction model using data from a public database. The radar sensor evaluates the measured object, obtains the pulse waveform data, calculates the pulse transmission time, and obtains the blood pressure data through linear model regression analysis. Confirm its availability to facilitate follow-up research, such as integrating other sensors, collecting temperature, heartbeat, respiratory pulse and other data, and seeking medical treatment in time in case of abnormalities.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.67-73
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• 2019

The blood pressure measurement is calculated as a value corresponding to the pressure of the blood vessel using the pressure from the outside for a long time. Due to the recent miniaturization of measurement equipment and the ICT combination of personal healthcare systems, a system that enables continuous and real-time measurement of blood pressure with a sensor is required. In this study, blood pressure was measured using pulse transit time using Photoplethysmography. In this study, blood pressure was estimated by using systolic blood pressure. And it is possible to make measurement only with PPG itself, which can contribute to making a micro blood pressure measuring device. As a result, systolic blood pressure and PPG's S1-P and P-S2 were used to analyze the possibility of blood pressure estimation.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.281-286
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• 2003

Blood Pressure is one of the most fundamental Parameters which reflects physical conditions medically and the blood pressure measurement system using oscillometric method is a Non-Invasive Blood Pressure measurement device by measuring arterial Pressure through a cuff. In this paper. we designed a inflatable wrist blood pressure system which measures blood Pressure during the stepping inflation in the wrist cuff. The hardware system consists of a main power unit, a bladder in cuff unit, signal detection units, signal Processing units. a wireless data transmission unit, and a data display unit. We evaluated the reliability of this system by comparing and analyzing systolic. diastolic blood Pressure, and heart rate with other commercial blood Pressure measurement devices. Characteristic ratio values used to determine systolic and diastolic blood Pressure using MAA(Maximum Amplitude Algorithm) were 0.436 and 0.671 respectively.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.53-58
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• 2019

Recently, product research has been continuously conducted to enhance accessibility to blood pressure measurement for the purpose of healthcare for the chronic patient. In previous studies, electrocardiogram (ECG) and photoelectric pulse wave (PPG) are analyzed to calculate systolic and diastolic blood pressure. The problem is the development of analysis algorithms for accuracy and reproducibility. In this study, in the development stage of a micro blood pressure measuring device, the size of the device was reduced and the measurement method was simplified, while the algorithm was to extract systolic blood pressure (SBP) using only two PPGs and obtain diastolic blood pressure (DBP). The difference value of PPG, DF_P, is inversely related to SBP, and has a proportional relationship with DBP, which can be leaked by algorithm, and DBP can be tracked through SBP.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.62-66
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• 2014

This study aims to implement the emergency monitoring robot system which predicts the current state of the patients without visiting the medical institutions by measuring the basic health status of the user's blood pressure, heartbeat, and basic health status of body temperature in the disaster emergency situation based on the Smart Grid. By arranging a large number of sensor(blood pressure, heartbeat, body temperature sensor) and measuring the bio signs, so the attached wireless XBee sensor can be stored in DB of robot, and it aims to draw the current state of the patients by analysis of stored bio data. Among 300 data obtained from the sensor, 1st data to 100th data were used for learning, and from 101st data to 300th data were used for assessment. 12 results were different among the total 300 assessment data, so it shows about 96% accuracy.

• Journal of Sensor Science and Technology
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• v.20 no.5
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• pp.343-350
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• 2011

The compliance and stiffness of artery are closely related with disease of arteries. Pulse wave velocity(PWV) in the blood vessel is a basic and common parameter in the hemodynamics of blood pressure and blood flow wave traveling in arteries because the PWV is affected directly by the conditions of blood vessels. However, there is no standardized method to measure the PWV and it is difficult to measure. The conventional PWV measurement has being done by manual calculation of the pulse wave transmission time between coronary arterial proximal and distal points on a strip chart on which the pulse wave and ECG signal are recorded. In this study, a pressure sensor consisting of strain gauges is used to measure the blood pressure of arteries in invasive method and regular ECG electrodes are used to record the ECG signal. The R-peak point of ECG is extracted by using a reference level and time windowing technique and the ascending starting point of blood pressure is determined by using differentiation of the blood pressure signal and time windowing technique. The algorithm proposed in this study, which can measure PWV automatically, shows robust and good results in the extraction of feature points and calculation of PWV.