• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.887-889
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• 2016

본 연구는 전도성 섬유를 양말에 부착하여 발바닥에서 심전도(ECG, Electrocardiogram) 신호를 검출하였다. 발바닥에서 측정한 심전도 신호와 손목에서 측정한 심전도 신호에 Pan-Tompkins algorithm을 적용하였고 R-R interval을 검출하였다. 이후 발바닥과 손목에서 측정된 심전도의 유의성을 검출하기 위해 비모수 검정법인 Spearman검정을 사용하여 상관분석을 수행하였다. 상관분석 결과, 유의확률 p=0.00에서 correlation coefficient=0.901로 두 데이터는 강한 양의 선형 관계에 있는 것으로 나타났다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.336-337
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• 2016

FHIR is a protocol that enables easy data exchange from the time the event occurred in health care settings as a standard for next-generation message exchange of HL7. Create a meaningful message from the ECG and medical equipment, and express the messages generated by standardized FHIR message it will be used in various medical institutions to ensure delivery to EMR, such as hospital information systems can query the results via a smartphone.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.75-82
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• 1999

This paper describes the implementation of wireless realtime monitoring system using modified CSMA/CA protocol. This system consists of wireless modem, central monitor, mobile station, DB server, and offer advantage of mobility, reduced installation time, long-term cost savings, and so on. And this system offers patient position pursuit service. Patient position pursuit service must be offered to deal with emergency which can be occured during patient movement. This paper proposes modified CSMA/CA protocol and patient position pursuit algorithm, implements wireless realtime monitoring system using it.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1924-1925
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• 2007

본 논문에서는 부정맥 판별을 위한 전처리 과정으로 PCA, LDA, ICA를 바탕으로 하여 정확도를 비교하여 보았다. 각각의 전처리는 고유의 특성을 가지고 있으며 본 논문의 목적은 부정맥 판별상 어떤 전처리가 더욱 정확성의 면에서 효과적인지를 알아보는 것이다. 본 논문의 데이터는 MIT-BIH에 기반하고 있으며, Beat의 분류는 정상(Normal), 좌각차단(Left Bundle Branch Block, LBBB), 우각차단(Right Bundle Branch Block, RBBB), 조기심실수축(Premature Ventricular Contraction, PVC), 조기심방수축(Atrial Premature Beat, APB), paced Beat, 심실보충수축(Ventricular Escape Beat)로 나누었다. 실험적 결과는 PCA-BPNN의 경우 95.53%, ICA-BPNN의 경우 93.95%, LDA-BPNN의 경우 96.42%로 LDA가 가장 ECG 부정맥 판별 응용에 있어 가장 효율적인 방법으로 나타났다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.276-277
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• 2014

일상생활 중 장시간 계측하는 심전도 모니터링의 경우 250Hz~500Hz 또는 그 이상의 높은 샘플링 속도가 요구된다. 그러나 일상생활 중 장시간 계측을 위해 소형화된 제한적인 기기로는 기하급수적으로 늘어나는 데이터를 감당하기가 어렵다. 따라서 본 연구에서는 한정된 자원을 효율적으로 이용하기 위한 템플릿 매칭 기반의 심전도 압축 알고리즘을 제안하였으며 모바일 어플리케이션에 구현하고자 하였다. 그 결과 CR평균은 5.56 PDR평균은 5.33으로 나타났으며, 모바일 어플리케이션에 적용하여 그 유용성을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.19-22
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• 2008

최근 서구화된 식생활 패턴과 흡연, 비만 등의 원인으로 인해 심근경색, 협심증과 같은 심근허혈(myocardial ischemia) 질환이 급증하고 있다. 이 논문에서는 심전도 신호로부터 허혈성 심장 질환 진단을 위해 출현 패턴 마이닝을 이용하여 심근경색 및 협심증의 진단 신호인 ischemia beat를 분류 하였다. 또한 기존의 출현 패턴 마이닝에 빠른 패턴 탐사와 저장 공간의 효율성을 고려하여 Apriori-T 빈발 패턴 탐사 알고리즘을 출현 패턴 생성이 가능하도록 확장하였다. PhysioNet의 ST-T 데이터베이스로부터 138개의 대조군(정상)과 ischemia beat 데이터에 제안된 분류 알고리즘을 실험한 결과 최소 75% 및 최대 95%의 예측 정확도를 보였다.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.87-91
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• 2017

Recently, several kinds of researches using IoT wearable devices are active in the field of sports, design, emotional sciences and so on. The human bio data such as blood pulse, ECG, SKT signal, and GSR Signal producing from IoT wearable devices such as Watch, Smart-band, Grass can adapt to the meaningful future applications. Using the human's emotional data and a physical status with variation and so on, we can individually get the personal status. Due to knowing the personal emotion or physical status is related and connected to the valuable wallet of customers, the approach is more important in nowadays. Therefore, the personal information can effectively adapt to the marketing of the culture industry, which deals with emotions of customers. The research shows implementation steps for explaining overall architecture of the convergence research between Art and Technologies.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.61-70
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• 2017

Physiological responses have been measured to recognize emotion. Although physiological responses have been interrelated between organs, their connectivities have been less considered for emotion recognizing. The connectivities have been assumed to enhance emotion recognition. Specially, autonomic nervous system is physiologically modulated by the interrelated functioning. Therefore, this study has been tried to analyze connectivities between heart and respiration and to find the significantly connected variables for emotion recognition. The eighteen subjects(10 male, age $24.72{\pm}2.47$) participated in the experiment. The participants were asked to listen to predetermined sound stimuli (arousal, relaxation, negative, positive) for evoking emotion. The bio-signals of heart and respiration were measured according to sound stimuli. HRV (heart rate variability) and BRV (breathing rate variability) spectrum were obtained from spectrum analysis of ECG (electrocardiogram) and RSP (respiration). The synchronization of HRV and BRV spectrum was analyzed according to each emotion. Statistical significance of relationship between them was tested by one-way ANOVA. There were significant relation of synchronization between HRV and BRV spectrum (synchronization of HF: F(3, 68) = 3.605, p = 0.018, ${\eta}^2_p=0.1372$, synchronization of LF: F(3, 68) = 5.075, p = 0.003, ${\eta}^2_p=0.1823$). HF difference of synchronization between ECG and RSP has been able to classify arousal from relaxation (p = 0.008, d = 1.4274) and LF's has negative from positive (p = 0.002, d = 1.7377). Therefore, it was confirmed that the heart and respiration to recognize the dimensional emotion by connectivity.

• Journal of Biomedical Engineering Research
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• v.24 no.1
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• pp.1-8
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• 2003

The job of long distance driving is likely to be fatiguing and requires long period alertness and attention, which make considerable demands of the driver. Driving fatigue contributes to driver related with accidents and fatalities. In this study, we investigated the relationship between the number of hours of driving and driving fatigue using heart rate variability(HRV) signal. With a more traditional measure of overall variability (standard deviation, mean, spectral values of heart rate). Nonlinear characteristics of HRV signal were analyzed using Approximate Entropy (ApEn) and Poincare plot. Five subjects drive the four passenger vehicle twice. All experiment number was 40. The test route was about 300Km continuous long highway circuit and driving time was about 3 hours. During the driving, measures of electrocardiogram(ECG) were performed at intervals of 30min. HRV signal, derived from the ECG, was analyzed using time, frequency domain parameters and nonlinear characteristic. The significance of differences on the response to driving fatigue was determined by Student's t-test. Differences were considered significant when a p value < 0.05 was observed. In the results, mean heart rate(HRmean) decreased consistently with driving time, standard deviation of RR intervals(SDRR), standard deviation of the successive difference of the RR intervals(SDSD) increased until 90min. Hereafter, they were almost unchanging until the end of the test. Normalized low frequency component $(LF_{norm})$, ratio of low to high frequency component (LF/HF) increased. We used the Approximate Entropy(ApEn), Poincare plot method to describe the nonlinear characteristics of HRV signal. Nonlinear characteristics of HRV signals decreased with driving time. Statistical significant is appeared after 60 min in all parameters.

• Journal of Yeungnam Medical Science
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• v.18 no.1
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• pp.39-50
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• 2001

Background: Power spectrum analysis method is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, we developed a data acquistion system for estimating the activity of the autonomic nervous system by the analysis of heart rate and respiratory rate variability using power spectrum analysis. Materials and methods: For the detection of QRS peak and measurement of respiratory rate from patient's ECG, we used low-pass filter and impedence method respectively. This system adopt an isolated power for patient's safety. In this system, two output signals can be obtained: R-R interval heart rate) and respiration rate time series. Experimental ranges are 30-240 BPM for ECG and 15-80 BPM for respiration. Results: The system can acquire two signals accurately both in the experimental test using simulator and in real clinical setting. Conclusion: The system developed in this paper is efficient for the acquisition of heart rate and respiration signals. This system will play a role in research area for improving our understanding of the pathophysiologic involvement of the autonomic nervous system in various disease states.