• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.61-66
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• 2021

This study aimed to investigate the effect of changes in pain on the autonomic nervous system and brain waves after inducing delayed-onset muscle soreness(DOMS). Based on voluntary participation, 28 participants with induced-DOMS were randomly divided into control(non-treatment, n=14) and experiment groups(transcutaneous electrical nerve stimulation (TENS) and kinesio taping, n=14). Intervention was performed from first day to fifth days after the onset of DOMS. Measurements were performed using the Visual Analogue Scale(VAS), Heart Rate Variability(HRV), and brain wave before DOMS induction, 24 hours after, fifth day after, and eighth day after. According to the study results, when DOMS occurred, the activity of the sympathetic nervous system was increased or the activity of the parasympathetic nervous system was suppressed, and reduction of pain due to interventions showed the opposite activity. A decreased in alpha was seen during pain, but was not significant. These results will help develop and study pain management and treatment strategies.

• 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.

• 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.

• 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

• Annual Conference of KIPS
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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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• 2009.05a
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• pp.209-210
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• 2009

수면단계는 수면감성을 평가하는데 있어서 중요한 생리지표로서 사용되어왔다. 그러나 수면다원검사를 이용한 전통적 수면단계 분류방법은 뇌전도, 안전도, 심전도, 근전도 등을 종합적으로 측정하므로 수면단계를 비교적 정확히 분류할 수 있지만 피험자에게 심한 구속감을 주는 문제가 있다. 본 연구에서는, 각성상태에서 교감신경계가 지배적인 반면에 수면 중에는 부교감신경계가 더 활동적인 점에 착안하여 수면단계를 간단히 분류할 수 있는 방법을 찾고자 수면단계에 따른 심박동변이도(heart rate variability: HRV)를 분석하였다. 단일채널 심전도를 이용하여 수면단계별로 HRV 의 교감신경계/부교감신경계 활성도의 비율을 분석한 결과, W(wakefulness) 단계가 NREN(non REM) 2 단계, 3 단계, 4 단계에 비하여 높게 나타났으며, NREM 4 단계는 REM(rapid eye movement) 단계와 NREM 1단계에 비하여 낮게 나타났다. 또한 교감신경계/부교감신경계 활성도 비율의 수면단계에 따라 변화하는 양상은 W, REM, NREM 1, 2, 3, 4 단계의 순으로 단조 감소하였다.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.1-9
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• 2012

This paper presents an algorithm for depression diagnosis using the Neural Network with Weighted Fuzzy Membership functions (NEWFM) and heart rate variability (HRV). In the algorithm, 22 different features were initially extracted from the HRV signal by frequency domain, time domain, wavelet transformed, and Poincar$\acute{e}$ transformed feature extraction methods; of these 6 optimal features were selected by significance evaluation using Non-overlap Area Distribution Measurement (NADM) based on NEWFM. The proposed algorithm uses these 6 optimal features to diagnose depression with an accuracy of 95.83%.

• 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 Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.61-69
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• 2015

This study presents a probabilistic knowledge discovery method to interpret heart rate variability (HRV) based on time and frequency domain indexes, extracted using discrete wavelet transform. The knowledge induction algorithm was composed of two phases: rule generation and rule estimation. Firstly, a rule generation converts numerical attributes to intervals using ROC curve analysis and constructs a reduced ruleset by comparing consistency degree between attribute-value pairs with different decision values. Then, we estimated three measures such as rule support, confidence, and coverage to a probabilistic interpretation for each rule. To show the effectiveness of proposed model, we evaluated the statistical discriminant power of five rules (3 for atrial fibrillation, 1 for normal sinus rhythm, and 1 for both atrial fibrillation and normal sinus rhythm) generated using a data (n=58) collected from 1 channel wireless holter electrocardiogram (ECG), i.e., HeartCall$^{(R)}$, U-Heart Inc. The experimental result showed the performance of approximately 0.93 (93%) in terms of accuracy, sensitivity, specificity, and AUC measures, respectively.