• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1257-1264
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• 2019

The traditional cuff-based method for BP(Blood Pressure) measurement is not suitable for continuous real-time BP measurement techniques. For this reason, the previous studies estimated various blood pressures by fusion with the electrocardiography (ECG) and photoplethysmogram (PPG) sensor signals. However, conventional techniques based on PPG bio-sensing measurement face many challenging issues such as noisy supply fluctuation, small pulsation, and drifting non-pulsatile. This paper proposed a novel BP estimation methods using PPG and ECG sensors, which can be derived from the relationship between PPG and ECG using PTT(Pulse Transit Time) and PWV(Pulse Wave Velocity). Unlike conventional height ratio features, which are extracted on the basis of the peaks in the PPG and ECG waveform. The proposed method can be reliably obtained even if there are missing peaks among the sensed PPG signal. The increased reliability comes from periodical estimation of the peak-to-peak interval time using ECG and PPG. After 250,000 times trials of the blood pressure measurement, the proposed estimation technique was verified with the accuracy of ±28.5% error, compared to a commercialized BP device.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.1-10
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• 2020

In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.334-340
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• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.1009-1011
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• 2013

최근 U-헬스케어 서비스가 증가함에 따라 예방과 건강증진 등에 관심과 연구가 활발히 이루어지고 있다. 이러한 U-헬스케어에 사용되는 ECG, EMG, PPG EEG등이 스마트폰 기반의 휴대용 장비와 연동된 센서들의 연구 역시 활발히 진행 중이다. 일반적으로 PPG신호는 적외선센서를 이용하여 모세혈관의 혈중 헤모글로빈 농도 변화에 의한 맥파를 측정한다. PPG신호의 잡음을 제거하기 위해 필터를 사용하였다. 필터된 정보를 ADC하여 스마트폰으로 BlueTooth통신을 이용하여 전송한다. 최종적으로 스마트 디바이스에 PPG신호를 그린다. 제작결과 PPG아날로그 신호를 보는 것과 동일하게 스마트 디바이스에 그릴 수 있음을 보였다. 이후 PPG신호를 이용하여 혈관건강도 및 건강예방의 측정의 연구를 계속해서 한다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2007-2009
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• 2008

본 논문에서는 인체에 열을 가했을 때 심전도(ECG) 신호에서 얻어진 Heart Rate Variability(HRV)와 광전용적맥파(PPG)에서 얻어진 Photoplethymograph Variability(PV)의 상관관계를 보기 위하여 피실험자가 상온에서 누워 있을때와 65$^{\circ}C$의 온도로 누워있을때의 심전도와 PPG를 측정하여 두 파라메터의 상관관계를 분석하여 그 효용성을 평가하였다. 이를 위하여 우선 신체 건강한 26세에서 32세까지 총 3명의 남성을 대상으로 하여 ECG와 PPG를 위와 같은 조건에서 측정한 다음, 두 신호로부터 피크를 검출하고, 피크 간격을 시계열 신호로 변환하여 HRV와 PV신호를 얻어 내었다. 또한 얻어진 HRV신호와 PV신호의 주파수 분석을 통하여 HF 및 LF성분을 비교하여 HRV와 PV의 상관관계를 비교하였다.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.137-142
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• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.35-38
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• 2010

본 논문에서는 일상생활 속에서 무자각적으로 생체신호를 측정하고 분석하여 감성을 평가할 수 있는 임베디드 시스템에 관하여 연구하였다. 지속적으로 변화하는 감성을 일관적이며 신뢰성이 높은 생리적인 방법으로 평가하기 위해 심전도(ECG:Electrocardiogram), 맥파(PPG:Photoplethysmogram)의 두 가지 생체신호를 측정하고, 무선전송(Bluetooth) 장치를 이용하여 측정한 생체신호를 실시간으로 노트북PC로 전송하여 분석하였다. 생체신호의 분석방법은 FFT(Fast Fourier Transform)과 전력스펙트럼밀도(Power Spectrum Density)를 이용한 주파수 분석방법으로 두 생체신호의 특정 주파수 대역이 가지는 자율신경계의 활성도의 비율을 분석하여 비교 연구하였다. 또한 보다 빠르고 정확한 감성을 평가하기 위하여 분석알고리즘의 연산을 최소화 하였으며 그래프를 이용한 분석결과의 시각화를 하였다. 본 논문에서는 무자각적인 생체신호 측정 시스템을 이용하여 다양한 상황에서 생체신호를 측정하고, 개발한 분석 알고리즘으로 분석한 결과의 차이를 연구하여 정확도 및 신뢰도를 기준으로 감성을 평가하기 위한 분석 시스템을 평가하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.624-627
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• 2008

본 연구에서는 유비쿼터스 헬스케어를 위하여 비침습적으로 측정기 가능하고, 많은 건강정보를 포함하고 있는 심전도(electrocardiogram, ECG)와 광전용적맥파(photo plethysmograph, PPG)를 측정하고자 하였다. 이를 위하여 배터리로 구동 가능한 초소형의 심전도 및 맥파측정 시스템의 구현을 위하여 각각의 신호를 검출 및 신호처리하기 위한 회로를 구현하였다. 그리고 계측된 심전도 및 맥파신호의 무선전송을 위하여 초저전력 무선센서네트워크 기술을 적용한 무선 생체신호 전송시스템을 구현하였다. 계측된 심전도의 R파 정점과 인체의 말초부위에서 측정한 맥파의 기준점 사이의 시간인 맥파전달시간(pulse transit time, PTT)을 분석하여 심장에서 말초부위까지 혈관의 물리적 특성을 평가함으로써 동맥경화와 같은 혈관질환의 사전모니터 링이 가능한 시스템을 구현하고자 하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.992-999
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• 2009

The pulse transit time(PTT), which is determined by measuring the electrocardiogram(ECG) and pulse wave, gives comprehensive information about the cardiovascular system. However, a little movement of body and/or inaccurate pressure applied to skin during the measurement of pulse wave leads to acquire incorrect results. To overcome such problem, we developed an integrated sensor system which makes it possible to measure ECG, pressure pulse wave(PPW) and photoplethysmograph(PPG) at the same time. Futhermore, we implemented a new metal electrode which enables to continuously measure ECG. We verified that both integrated sensor system and new electrode provide useful effect.