• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.107-116
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• 2009

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

• Journal of Life Science
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• v.19 no.2
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• pp.271-276
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• 2009

The Purpose of this study was to examine the effects of boxing aerobic training on resting heart rate variability (HRV) in females. The subjects for this study were performed 16 female college students that aged 19-23. The subjects were divided into two groups; boxing aerobic exercise group (9 students) and control group (7 students). Boxing aerobic training program was performed three times a week for twelve weeks with $VO_{2max}$ 60-80% exercise intensity. The results of this study were following; 1. Mean HRT (mean heart rate) had no signigicant difference among the groups and exercise time. 2. SDNN (standard deviation of the normal to normal interval) had no significant difference among the group and exercise time. 3. RMS-SD (square root of the mean squared differences of successive normal to normal interval) had no significant difference among the groups and exercise time. 4. TP (total power) had no significant difference among the groups and exercise time. 5. LF (low frequency) had no significant difference among the groups and exercise time. 6. HF (high frequency) had no significant difference among the groups and exercise time. 7. LF/HF (low frequence/high frequency ratio) had no significant difference among the groups and exercise time. 8. VLF (very low frequency) had no significant difference among the groups and exercise time.

• Science of Emotion and Sensibility
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• v.15 no.3
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• pp.345-354
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• 2012

The purpose of this study is finding alternative parameters of the HRV so as to minimize the subjective errors by STAI and BDI, could be have two types of significant correlation levels depending on normalized method. Particularly, the LF/HF presented as the quantitative physiological parameter that can reflect both state-anxiety and trait-anxiety.

• Korean Journal of Acupuncture
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• v.24 no.2
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• pp.185-191
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• 2007

목 적 : 전침 자극은 최근에 임상연구와 더불어 기초연구에서 자주 사용하는 방법으로 경락이론에 의거하여 신체에 분포되어 있는 경락상의 각종경혈부위에 인위적인 전기 자극을 통하여 질병을 치료, 예방 혹은 완화하는 방법이다. 이에 본 연구에서는 정상적인 쥐의 상태에서 일정한 전침 자극을 주었을 때, 심박변이도의 변화를 측정 하였다. 방 법 : 쥐의 정상적인 마취상태에서 태충혈에 4 Hz와 80 Hz의 5 V 크기로 10 mA 강도의 사각파 파형으로 자극을 주었으며 대조조로 비경혈 부위에 4 Hz의 동일한 파형으로 자극을 주어 심박 변이도의 변화정도를 측정하였다. 결 과 : 본 실험에서 RR Interval의 경우 태충혈 4 Hz 보다 80 Hz일때 RR Interval의 파형이 더 크게 나타났으며, PSD의 분석에서도 자극 전후를 비교하였을 때 $1{\sim}3$ Hz 사이에 높은 피크가 보였으며 4 Hz의 비경혈 부위에서는 큰 변화를 보이지 않았다. 결 론 : 이번 실험연구를 통하여 정상적인 쥐의 상태에서 일정한 전침 자극을 주었을 때, 심박 변이도 변화를 도출할 수 있었으며, 이번 실험을 통해 비수술적인 방법으로 치료효과와 심박 변이도를 측정할 수 있었고, 이를 통해 전침자극의 기전 연구에 있어 과학적 근거를 제시할 수 있다고 사료된다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1973-1974
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• 2008

심전도는 심장의 전기적인 활동을 나타내는 지표로, 심장활동 및 자율 신경계를 관찰하기 위해 R 파형의 간격을 측정함으로써 심박변이도를 추출할 수 있다. 심박변이도는 자율신경계의 관찰 이외에도 급성 심근경색의 예후판정이나 심장급사 예견, 당뇨병 관찰, 감성 및 스트레스 평가 등 다양한 분야에서 활용되고 있다. 그러나 심전도의 추출해야하는 과정을 거쳐야 하기 때문에, 임상적인 목적이 아닐시에도 3 전극을 몸에 부착하여 측정하는 것은 심박변이도에 대한 접근성을 떨어뜨리고 있다. 본 연구에서는 심박변이도의 측정을 위하여 수정된 방식의 라플라시안 전극을 설계하고 이를 이용해 심박변이도의 측정 및 분석을 함으로써 접근성 높은 새로운 심박변이도 측정용 전극에 대해 제시하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.2
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• pp.43-49
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• 2010

Autonomic nervous system (ANS) acts as a control system functioning largely below the level of consciousness, and controls visceral functions. The activity of the ANS has been assessed by means of the heart rate variability (HRV). It has been reported that HRV is dependent on sex, age, body mass index, and smoking, etc. However, the effects of posture and sleep deprivation on HRV have rarely been reported. Objective of our work was to find out which posture is appropriate for stable HRV. We measured the number of sleep deprivation and HRV using power spectrum in six stages for 30 minutes. Increased low frequency (LF) power and high frequency (HF) power indicate enhanced sympathetic and parasympathetic activity, respectively. We determined the LF/HF ratio to minimize individual difference. It was found that sleep deprivation by awakening up subjects was affected by posture, which resulted in changes of LF/HF. Although LF/HF varied with time, it was more stable in sitting than in supine. In conclusion, we recommend sitting posture when measuring HRV because of less sleep deprivation resulting in less variation in LF/HF.

• 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.

• Journal of the Korean Society of Radiology
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• v.5 no.1
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• pp.19-25
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• 2011

Photoplethysmogram(PPG) is the method to obtain the biomedical signal using the linear relationships between the blood volume for changing the cardiac contraction and relaxation and the amount of light for absorbing the hemoglobin in the blood. In this paper, we proposed the analyzed results which show the heart rate variability and the distribution of heart rate for before and after using PPG. Moreover, this paper designed and implemented the system based on personal computer to predict cardiovascular disease in advance using the analyzed results for the autonomic balance from taking the spectral analysis of heart rate and the state of the blood vessel for analyzing APG(acceleration plethysmogram).

• 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%.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.1012-1013
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• 2014

본 논문에서는 일상생활 중 지속적으로 심장 상태를 모니터링 할 수 있는 무구속 의자형 심탄도 측정시스템을 개발하였다. 또한 구현된 시스템에서 측정된 생체신호를 이용하여 주관적인 감정자극의 스트레스를 분석하기 위한 연구를 수행하였다. 수준을 분석하고자 하였다. 실험은 시스템에 착석하여 실시간으로 시청각 자극 실험을 수행하였고, 심박수와 심박변이도의 시간영역 및 주파수영역 파라미터를 확인하였다. 확인된 심박변이도의 파라미터는 시청각 도중 기술한 인간의 감정들을 체계화하여 2차원 공간에 여러 감정들의 관계를 나타낸 제임스 러셀(J. Russell)의 감정모델을 주관적인 감정 자극에 의한 스트레스 지표 나타내어 비교 분석하였다. 실험결과는 RMSSD, LF/HF 파라미터가 스트레스 수준 분류에 사용될 수 있는 잠재력을 가지고 있음을 증명한다.