• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.294-294
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• 2000

In this paper, we would like to discuss the signal processing and the algorithm for ECG analysis. The ECG gives us information about the condition of the heart muscle, because myocardial abnormality or infarction is inscribed on the ECG during myocardial depolarization and repolarization. Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. The wavelet transform decomposes the ECG signal into high and low frequency component using wavelet function. Recomposing high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the curve-fitting partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with some kinds of heart disease ECG pattern, we can detect and classify the kind of heart disease.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.293-299
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• 2004

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.7-14
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• 2014

Noncontact electrocardiogram (ECG) measurement using capacitive-coupled technique is a very reliable long-term noninvasive health-care remote monitoring system. It can be used continuously without interrupting the daily activities of the user and is one of the most promising developments in health-care technology. However, ECG signal is a very small electric signal. A robust system is needed to separate the clean ECG signal from noise in the measurement environment. Noise may come from many sources around the system, for example, bad contact between the sensor and body, common-mode electrical noise, movement artifacts, and triboelectric effect. Thus, in this paper, the extended Kalman filter (EKF) is applied to denoise a real-time ECG signal in capacitive-coupled sensors. The ECG signal becomes highly stable and noise-free by combining the common analog signal processing and the digital EKF in the processing step. Furthermore, to achieve ubiquitous monitoring, android-based application is developed to process the heart rate in a realtime ECG measurement.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1085-1087
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• 2015

본 논문에서는 심장의 전기활성도를 반영하는 ECG 신호 중 일부를 명세한다. 꾸준히 축적되었고 통용되는 ECG 신호의 비정형 명세를 정형 명세로 바꾸는 과정에서 선형 시제 논리보다 시간을 다루는 명세 및 양적 평가에 유리한 신호 시제 논리(Signal Temporal Logic)를 사용한다. ECG 신호를 감지했다는 가정하에 특징점을 추상화하여 신호를 맹세했고, 양적으로 평가해주는 모델 기발 실시간 ECG 모니터링 시스템의 신속한 개발 필요성을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.414-421
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• 2015

This paper presents a new method to decompose a duplicated ECG signal, which is measured from two people, to two individual ECG signals. In paper, it is shown that the duplicated ECG signal can be decomposed, provided that their SAECG signals are known. As the SAECG signal is the average of a ECG signal, it is a feature to identify individual ECG signals from the duplicated signal. Since the ECG signal is nearly periodic, so-called heart-rate, the period of each ECG signal can be found by using the autocorrelation of the duplicated signal, That is, the autocorrelation has high peaks at the multiple instants of heart-rate of each person. With the heart-rate of each person obtained by some processing, all R-peaks are identified by the SAECG signals. To be concrete, the SAECG signal of each person is repeatedly placed at the R-peak instants with his heart-rate, and the weight of each SAECG signal is computed by LMSE optimization. Finally, as adding the error signal in the LMSE optimization processing to the weighted SAECG signal, each individual ECG signal is obtained. In experimental results, we demonstrate that the duplicated ECG signal is successfully decomposed into two ECG signals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1287-1294
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• 2001

As size of waveform is very small, ECG(electrocardiogram) signal is difficult to analyze for noise which is occurred when it measures. In order to obtain ECG clearly, it must eliminate that power line interference, baseline wandering, noise of muscle constriction. In ECG, the worst problem which is recorded signal of ECG is the baseline wandering elimination, which is occurred by rhythm of respiration and muscle constriction of part from attaching to an electrode. Such the baseline is roughly irregular wandering and shaking up and down therefore the part of the baseline wandering elimination is very important because it is difficulty of ECG diagnosis. In this study, as implementation of real-time signal processing digital filter it is applicable to analyze patient's heart disease by way of design of the baseline wandering elimination system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.989-993
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• 2008

본 연구에서는 생체신호 중 비침습적으로 측정이 가능하고 많은 건강정보를 포함하고 있는 ECG(electrocardiogram)신호를 일상생활 중 보다 편리하게 모니터링 할 수 있는 시스템을 구현하고자 하였다. 이를 위하여 벨트형 ECG전극 시스템을 개발하였으며, 배터리로 구동 가능한 초소형 저전력 ECG측정시스템을 구현하였다. 또한 측정된 ECG신호의 무선전송을 위하여 Zigbee호환 무선센서노드를 이용하여 초저전력 무선데이터 통신부를 구성하였고 PC상에서 ECG신호를 모니터링하기 위한 프로그램을 구현하였다. 그리고 ECG측정 시 움직임에 따라 발생하는 동잡음의 제거를 위하여 이동평균필터(moving average filter)를 이용하여 기저선 변화를 추출하였고 이를 적응필터의 참조신호로 사용하여 동잡음을 제거하였다. 실험 결과 본 연구에 의해 구현된 ECG전극 및 계측시스템을 통해 활동상태 에서도 ECG계측 가능성을 확인하였으며, 제안한 적응신호처리기법을 통해 활동 중 ECG측정에서 동잡음의 최소화가 가능함을 확인하였다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1444-1452
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• 1989

This paper describes the implementation of a real time fetal ECG monitoring system in which an adaptive multi-channel noise canceller is realized using the Texas Instruments TMS32020 progrmmmable ditital signal processor. An ECG signal from the electrode placed on the mother's abdomen and three ECGs from those on the chest are applied as the desired signal and the referened inputs, respectively, of the multi-channel filter. The coefficients of the filter are updated using the LMS algorithm such that the output of the multi-channel filter copies the maternal ECG embedded in the abdominal ECG. The enhanced fetal ECG is obtained by subtracting the filter output from the abdominal ECG, and the difference signal is recorded. Both off-line and on-line experimental results are presented to verify the effectiveness of the parameters for the digital signal processing algorithms and the prototype system.

• Journal of Sensor Science and Technology
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• v.29 no.3
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• pp.162-171
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• 2020

The incidences of cardiovascular diseases are rapidly increasing worldwide. The electrocardiogram (ECG) is a test to detect and monitor heart issues via electric signals in the heart. Presently, detecting heart disease in real time is not only possible but also easy using the myDAQ data acquisition device and LabVIEW. Hence, this paper proposes a system that can acquire ECG signals in real time, as well as detect heart abnormalities, and through light-emitting diodes (LEDs) it can simultaneously reveal whether a particular waveform is in range or otherwise. The main hardware components used in the system are the myDAQ device, Vernier adapter, and ECG sensor, which are connected to ECG monitoring electrodes for data acquisition from the human body, while further processing is accomplished using the LabVIEW software. In the Results section, the proposed system is compared with some other studies based on the features detected. This system is tested on 10 randomly selected people, and the results are presented in the Simulation Results section.