• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.275-283
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• 2001

This study was performed to extract and analyze the biosignals to find the relationship between the level of anesthesia and the variations of physiological parameters during epidural anesthesia. Seven male and twenty female patients(ages from 45 to 70 years old) were participated for the experiment, and ECGs, PPGs, SKTs, SCRs were obtained during anesthesia. As results, the HF/LF ratios of HRV were decreased after the injection anesthetics. For skin temperatures, values measured from the palm was reduced and the temperatures from four channels, measured from armpit through the right side of the body, were increased. SCRs were decreased for all channels after the injection of anesthetics. However the heart rate and PPGs showed no significant changes. It was concluded that the injection of anesthetics result the changes in biosignals, and it could be explained by the degree of the sympathetic and/or parasympathetic nerve activities. Results of this study could provide the valuable information for the estimation of level for the spinal and general anesthesia, and could be extended to the development of a system which could quantify the level of anesthesia.

• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.17-24
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• 2010

This paper describes a real time reliable monitoring method and analysis system using wrist type oximeter for ubiquitous healthcare service based on IEEE 802.15.4 standard. Photoplethysmograph(PPG) is simple and cost effective technique to measure blood volume change. In order to obtain and monitor physiological body signals continuously, a small size and low power consumption wrist type oximeter is designed for the measurement of oxygen saturation of a patient unobtrusively. The measured data is transferred to a central PC or server computer by using wireless sensor nodes in wireless sensor network for storage and analysis purposes. LabVIEW server program is designed to monitor stress indicator from heart rate variability(HRV) and process the measured PPG to accelerated plethysmograph(APG) by appling second order derivatives in server PC. These experimental results demonstrate that APG can precisely describe the features of an individual's PPG and be used as estimation of vascular elasticity for blood circulation.

• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.211-218
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• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.328-334
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• 2012

Continuous fetal heart beat monitoring has assisted clinicians in assuring fetal well-being during antepartum and intrapartum. Fetal heart rate (FHR) is an important parameter of fetal health during pregnancy. The Doppler ultrasound is one of very useful methods that can non-invasively measure FHR. Although it has been commonly used in clinic, inaccurate heart rate reading has not been completely resolved.. The objective of this study is to improve detection algorithm of FHR from Doppler ultrasound signal with simple method. We modified autocorrelation function to enhance signal periodicity and adopted adaptive window size and shifted for data segment to be analysed. The proposed method was applied to real measured data, and it was verified that beat-to-beat FHR estimation result was comparable with the reference fetal ECG data. This simple and effective method is expected to be implemented in the embedded system.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.23-29
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• 2021

[Purpose] This preliminary study aimed to develop a regression model to estimate the non-exercise activity thermogenesis (NEAT) of Korean adults using various easy-to-measure dependent variables. [Methods] NEAT was measured in 71 healthy adults (male n = 29; female n = 42). Statistical analysis was performed to develop a NEAT estimation regression model using the stepwise regression method. [Results] We confirmed that age^A, weight^B, heart rate (HR)_average^C, weight × HR_average^D, weight × HR_sum^E, systolic blood pressure (SBP) × HR_rest^F, fat mass ÷ height^2G, gender × HR_average^H, and gender × weight × HR_sum^I were important variables in various NEAT activity regression models. There was no significant difference between the measured NEAT values obtained using a metabolic gas analyzer and the predicted NEAT. [Conclusion] This preliminary study developed a regression model to estimate the NEAT in healthy Korean adults. The regression model was as follows: sitting = 1.431 - 0.013 × (A) + 0.00014 × (D) - 0.00005 × (F) + 0.006 × (H); leg jiggling = 1.102 - 0.011 × (A) + 0.013 × (B) + 0.005 × (H); standing = 1.713 - 0.013 × (A) + 0.0000017 × (I); 4.5 km/h walking = 0.864 + 0.035 × (B) + 0.0000041 × (E); 6.0 km/h walking = 4.029 - 0.024 × (C) + 0.00071 × (D); climbing up 1 stair = 1.308 - 0.016 × (A) + 0.00035 × (D) - 0.000085 × (F) - 0.098 × (G); and climbing up 2 stairs = 1.442 - 0.023 × (A) - 0.000093 × (F) - 0.121 × (G) + 0.0000624 × (E).

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.78-82
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• 2006

The ventricular assist device(VAD) helps to reduce the overload against the patient's native heart(NH). The pulsatile VAD pumps out the ventricular blood to the aorta with pulsatile flow. If the VAD pulsates simultaneously with the NH, the ventricle of the NH could confronts abnormally elevated aortic pressure, and this could deteriorate the ventricle rather than assist to recover it. Thus counterpulsation algorithms to avoid copulsation have been adopted by many VADs, but these methods utilize electrocardiography or arterial pressure signals, which may have difficulties to acquire consistently for a long period. In this study, the copulsation estimation algorithm for the counterpulsation is developed using the VAD outlet pressure signal. The VAD outlet pressure signal is good to maintain for a long time and the sensor part could be integrated to the VAD as a built-in module. From the VAD outlet pressure signal and its pump rate information calculated with Fast Fourier Transform, pulse peaks by the VAD and the NH were extracted and the next copulsation time at which the VAD and the NH would pulsate simultaneously was estimated. This estimation algorithm was implemented by using PC MATLAB software and tested for various pump rate conditions with mock circulation system. For each condition, the copulsation time was estimated successfully. Consequently, the results showed the possibility to use the outlet cannula pressure signal in the copulsation estimation.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.191-198
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• 1999

Biofeedback training is one of physiological self control methods for patients who has psychological problem and rehabilitational problem. It has been used to control blood pressure, heart rate, peripheral temperature, respiration, electromyography (ENG), and other biological signals-ENG, respiration, heat rate, peripheral temperature, skin conductance level-was developed in house. We applied this system to alcohol dependent patients to perform biofeedback training. In this experiment, the relaxation biofeedback training for alcohol dependent patient was carried out and the tension state for the change of biological signals were estimated using the fuzzy theory after relaxation biofeenback training. Eight alcohol dependent patients were agreed to participate in this experiment. Result showed that 1) the tension degree of patients were higher than the tension degree of normal subject. 2) The tension degree of patients were decreased as the training numbers were increased.

• 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.

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.161-167
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• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.