• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2265-2267
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• 2007

The purpose of this work is to compare the local fitting and global fitting approaches while applying regression model to the PTT-BP data for the prediction of exercise blood pressures. We used linear and nonlinear regression models to represent the PTT-BP relationship during exercise. PTT-BP data were acquired both under resting state and also after cycling exercise with several load conditions. PTT was calculated as the time between R-peak of ECG and the peak of differential photo-plethysmogram. For the identification of the regression models, we used local fitting which used only the resting state data and global fitting which used the whole region of data including exercise BP. The results showed that the global fitting was superior to the local fitting in terms of the coefficient of determination and the RMS (root mean square) error between the experimental and estimated BP. The nonlinear regression model which used global fitting showed slightly better performance than the linear one (no significant difference). We confirmed that the wide-range of data is required for the regression model to appropriately predict the exercise BP.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.917-927
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• 2018

High blood pressure causes various cardiovascular diseases and is associated with mortality. Periodic self-monitoring and recording of blood pressure is very helpful in preventing the occurrence of secondary diseases caused by hypertension. However, existing cuff-type blood pressure monitors have many limitations. As an alternative of that, a method of estimating the blood pressure by measuring the velocity change of the blood flow using the photo plethysmography is widely known. However, photo plethysmography have a low correlation with blood flow. So, we will propose an algorithm for estimating blood pressure using the relationship between velocity change of blood flow measured by magnetic field instead of photo plethysmography and electrocardiogram. For this purpose, First, we analyzed the correlation between photo plethysmography and magneto plethysmography. the correlation between MPG and PPG was r = 0.9449. Second, we compared estimated blood pressure and measured blood pressure. In the four experiment each result was r = 0.5737, r = 0.7863, r = 0.5669, and r = 0.7445.

• Journal of Biomedical Engineering Research
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• v.29 no.1
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• pp.40-45
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• 2008

Therapeutic ultrasound which is developed for rehabilitation therapy have already been used for healing joint contracture, synechia, acute and chronic inflammatory diseases. Medical devices for pain-relief and healing using therapeutic ultrasound are actively being developed. This study measured the change of PTT with the transmitted ultrasound through the human body to find out the increase of compliance of blood vessels. Measurement method of PTT in this study is employed as useful ways to acquire physiological information of patients in the clinical case in order to measure the change of mechanical characteristics of blood vessels. This study confirmed the PTT change of rehabilitation patients through the thermal effects of ultrasound by using PTT and also found that it is possible to increase PTT by adjusting the warm water and ultrasound. The increase of PTT means the decrease of the pulse wave velocity from the cardiovascular system to the peripheral arteries. The physiological effects occurred using the warm water and ultrasound.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1095-1101
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• 2008

The desirable method to diagnose abnormal blood pressure is to measure and manage blood pressure continuously and regularly. However, the sphygmomanometers that are based on a cuff have faults in that they can not measure the blood pressure continuously and they cause an unpleasant feeling. Therefore, it is essential to develop a new measuring method that causes no pain and that can obtain blood pressure continuously without any unpleasant feeling. Thus, we propose here a regression method to estimate the systolic blood pressure by using the PTTL(pulse transit time on leg) with some body parameters which are chosen from the relational analysis with systolic blood pressure. The data we use to make the regression model were obtained in triplicate from each of 50 males who were from 18 to 35 years. And we made estimation experiments of blood pressure on 10 males who did not take part in the making the regression model. According to the results, the proposed method showed a mean error of 4.00 mmHg and the standard variance was 2.45 mmHg. When we comparing the results of the proposed method with the rule of American National Standards Institute of the Association of the Advancement of Medical Instruments(ANSI/AAMI), the results satisfied the rule of a mean error less than 5 mmHg and a standard variance less than 8 mmHg. Therefore we were able to validate the usefulness of the proposed method.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.334-340
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• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.

• Transactions on Electrical and Electronic Materials
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• v.4 no.6
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• pp.9-12
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• 2003

The electrical properties of stabilized amorphous selenium typical of the material used in direct conversion x-ray imaging devices are reported. Carrier mobility was measured using time-of-flight (TOF) measurements to investigate the transport properties of holes and electrons in stabilized a-Se film. A laser beam with pulse duration of 5 ns and wavelength of 350nm was illuminated on the surface of a-Se with thickness of 400 $\mu\textrm{m}$. The photo induced signals of a-Se film as a function of time were measured. The measured transit times of hole and electron were about 8.73${\mu}\textrm{s}$ and 229.17${\mu}\textrm{s}$, respectively. The hole and electron drift mobilities decreases with increase of electric field up to 4V/$\mu\textrm{m}$. Above 4V/$\mu\textrm{m}$, the measured drift mobilities exhibited no observable dependence with respect to electric field. The experimental results showed that the hole and electron drifting mobility were 0.04584 $\textrm{cm}^2$ V$\^$-1/s$\^$-1/ sand 0.00174 $\textrm{cm}^2$V$\^$-1/s$\^$-1/ at 10 V/$\mu\textrm{m}$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.246-256
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• 2011

Due to the increasingly aging population, there is a rising demand for assistive living technologies for the elderly to ensure their health and well-being. The elderly are mostly chronic patients who require frequent check-ups of multiple vital signs, some of which (e.g., blood pressure and blood glucose) vary greatly according to the daily activities that the elderly are involved in. Therefore, the development of novel wearable intelligent systems to effectively monitor the vital signs continuously over a 24 hour period is in some cases crucial for understanding the progression of chronic symptoms in the elderly. In this paper, recent development of Wearable Intelligent Systems for e-Health (WISEs) is reviewed, including breakthrough technologies and technical challenges that remain to be solved. A novel application of wearable technologies for transient cardiovascular monitoring during water drinking is also reported. In particular, our latest results found that heart rate increased by 9 bpm (P < 0.001) and pulse transit time was reduced by 5 ms (P < 0.001), indicating a possible rise in blood pressure, during swallowing. In addition to monitoring physiological conditions during daily activities, it is anticipated that WISEs will have a number of other potentially viable applications, including the real-time risk prediction of sudden cardiovascular events and deaths.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.225-229
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• 1998

The purpose of the current study was comparative analysis of autonomic and electrocortical responses to passive and active touch of the tektites with different subjective emotional preference. Perspective goal of the project is development of a template for classification of tactile stimuli according to subjective comfort and associated physiological manifestations. The study was carried out on 36 female college students. Physiological signals were acquired by Grass and B10PAC 100 systems with AcqKnowledge III software. Frontal, parietal and occipital EEG (relative power spectrum /percents/ of EEG bands - delta, theta, slow and fast alpha, low and fast beta), and autonomic variables, namely heart rate (HR), respiratory sinus arrhythmia (RSA), pulse transit time (PTT), respiration rate (RSP) and skin conductance parameters (SCL, amplitude, rise time and number of SCRs) were analyzed for rest baseline and stimulation conditions. Analysis of the overall pattern of reaction indicated that autonomic response to tactile stimulation was manifested in a form of moderate HR acceleration, RSP increase, RSA decrease (lowered vagal tone), decreased n and increased electrodermal activity (increased SCL, several SCRs) that reflects general sympathetic activation. Parietal EEG effects (on contra-lateral side to stimulated hand) were featured by short-term alpha-blocking, slightly reduced theta and significantly increased delta and enhanced fast beta activity with few variations across stimuli. The main finding of the study was that most and least preferred textures exhibited significant differences in autonomic (HR, RSP, PTT, SCR, and at less extent in RSA and SCL) and electrocortical responses (delta, slow and fast alpha, fast beta relative power). These differences were recorded both in passive and active stimulation modes, thus demonstrating reproducibility of distinction between most and least emotionally preferred tactile stimuli, suggesting influence of psychological factors, such as emotional property of stimulus, on physiological outcome. Nevertheless, development of sufficiently sensitive .and reliable template for classification of emotional responses to tactile stimulation based on physiological response pattern may require more extensive empirical database.

• International Journal of Fluid Machinery and Systems
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• v.8 no.4
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• pp.264-273
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• 2015

This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.