• 순환기질환의공학회:학술대회논문집
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• 2004.11a
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• pp.26-27
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• 2004

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.271-280
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• 2011

Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.

• Science of Emotion and Sensibility
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• v.8 no.1
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• pp.47-54
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• 2005

This study is aimed to inspect how the different sensitivities in Behavioral activation system(BAS) and behavioral inhibition system(BIS) modulate on the properties of physiological responses stimulated by positive or negative affective sound. We measured the electroencephalogram(EEG) and electrocardiogram (ECG) of 32 students, consisted of four groups depending on the BAS and BIS sensitivities, during listening to meditation music or noise. The EEG was recorded at Fpl and Fp2 sites and Power spectral density(PSD) of HRV was derived from the ECG, and the power of HRV was calculated for 3 major frequency ranges(low frequency[LF], medium frequency and high frequency[HF]). After listening to music or noise, subjects reported the affect induced by the sound. For EEG, the power in the alpha band at Fp2, especially in the alpha-2 band(9.0-11.0 Hz) increased during the subjects listening to music, while the power at Fpl increased during noise. During listening to meditation music, there is a tendency that the left-sided activation in prefrontal cortex(PFC) is positively correlated with the difference of BAS(Z)-BIS(Z). During listening to noise, there is a tendency that the right-sided activation in PFC is dominant in case any of the sensitivity of BAS or BIS is high. For HRV, we found that the index of MF/(LF+HF), during listening to music, was higher significantly in the individuals with a low BIS but high BAS than in the individuals with a low sensitivity both BIS and BAS individuals. With high BIS, regardless of the BAS sensitivity, the difference of this index values was not significant. From these results we suggest that the physiological responses of different individuals in BAS and BIS react differently under the same emotionally provocative challenge.

• Journal of Korean Biological Nursing Science
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• v.17 no.2
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• pp.179-187
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• 2015

Purpose: This study was done to evaluate the effects of a recreation therapy program on burn rehabilitation patients to determine if the program is an effective nursing intervention which can affect mental health problems and heart rate variability. Methods: Subjects were 54 hospitalized burn rehabilitation patients (25 in the control group, 29 in the experimental group). The experimental group participated 6 times in a recreation therapy program led by a qualified instructor. Brief symptoms inventory-18 (somatization, anxiety, depression) questionnaire, and heart rate variability were checked before and after the recreation therapy program. Results: The mental health scale showed significant differences in somatization (p<.001), anxiety (p<.001) and depression (p<.001). There was no significant difference in heart rate variability (autonomic activity, autonomic balance, stress resistance, stress parameter and fatigue, mean heart rate, electro-cardiac stability). Conclusion: The findings from this study suggest that a recreation therapy program is an effective nursing intervention to decrease the level of mental health problems of burn rehabilitation patients. However, a subsequent study is needed to develop an intervention program that will induce the effect of physiological parameters like heart rate variability (HRV).

• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.151-158
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• 2003

Numerous studies of short-term, beat-to-beat variability in cardiovascular signals have used linear analysis techniques. However, no study has been done about the appropriateness of linear techniques or the comparison between linearities and nonlinearities in short-term, beat-to-beat variability. This paper aims to verify the appropriateness of linear techniques by investigating nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average(ARMA) with nonlinear neural network(NN) models for predicting current instantaneous heart rate(HR) and mean arterial blood pressure(BP) from past HRs and BPs. To evaluate these models. we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that 10 technique provides adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

• Science of Emotion and Sensibility
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• v.8 no.2
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• pp.95-101
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• 2005

This study is based on previous information regarding reduced cardiac vagal activity in patients with coronary artery disease(CAD), on reduced variance(SDNN : standard deviation of all normal RR intervals), low-frequency power(LF), and the complexity of heart rate variability(HRV) in patients with chronic heart failure(CHF), and on the normalized high-frequency power of HRV is the highest in the right lateral decubitus position among 3 recumbent postures in patients with CAD, However, nothing is known about the nonlinear dynamics of HRV for the 3 recumbent postures in patients with CAD. To investigate the linear and non-linear characteristics of HRV in patients with CAD, 29 patients as CAD group and 23 patients as control group were studied. Electrocardiogram(ECG) with lead II channel was measured on these patients for 3 recumbent postures in random order. The HRV from ECG was analyzed with linear method(for time and frequency domains) and nonlinear method. The lower the high-frequency power in normalized unit(nHF) in the supine or left lateral decubitous position, the higher the increase in nHF when the position was changed from supine or left lateral decubitous to right lateral decubitous. Among the 3 recumbent postures in patients with severe CAD, the right lateral decubitus position was observed to induce the highest vagal modulation, the lowest sympathetic modulation, and the highest complexity of human physiology system.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1689-1692
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• 2013

NUI(Natural User Interface)는 사용자가 신체부위를 사용하여 인터페이스 할 수 있는 기술이다. 본 연구에서는 NUI 공간에서 체감형 게임을 시행하였다. 게임은 태권도게임으로 사용자와 컴퓨터간의 대련이며, 게임 시 사용자의 심전도 신호를 측정하였다. 사용자는 심전도데이터를 게임 시에 사용자 프로파일로 전송한다. 전송받은 심전도신호로 사용자의 심박변이도를 분류하여 분류기 실험을 시행하고 정확도를 측정하였다. 실험은 체감형 게임 시행 전과 시행 후의 상태로 나누어 실험하였으며, 분류기는 Decision Tree를 사용하였다. 실험결과 심박변이율은 게임 시행 후 정확도가 4.16% 높게 도출되었다.

• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.29-29
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• 2004

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.457-458
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• 2019

본 논문에서는 심장 박동 변이 (HRV: heart rate variability) 센서와 표준 기반 사물인터넷 (IoT: Internet of Things) 플랫폼을 활용한 스트레스 측정 시스템을 구현하고 그 활용 방법을 제안한다. HRV를 측정하기 위해 맥파 (PPG)센서와 오픈소스 하드웨어를 이용해 센싱 시스템을 구축하고 표준 사물인터넷 플랫폼을 이용하여 서버 시스템을 구축하였다. 추후 분석을 통해 유추한 스트레스 정도에 따라 플랫폼에 연결된 다양한 기기들을 이용하여 스트레스를 낮추기 위한 개인별 스트레스 관리 솔루션을 구현할 수 있음을 알 수 있다.