• 센서학회지
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• 제23권6호
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• pp.402-408
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• 2014

Heart rate is one of the most important signal to monitor the health condition of the patient or exerciser. Various wearable devices have been developed for the continuous monitoring of ECG signal from human body during exercise. Among these, ECG chest belt has been widely used. However wearing chest belt with ECG sensor is uncomfortable in normal life due to the electrode contact between metal electrodes of ECG sensor and skin of the human body. So we develop the royal healthcare backpack that can measure ECG signal without skin contact by using capacitor-type ECG sensor. The position of the measurement point is critical to collect a clear ECG signal in the capacitive ECG measurement from backpack. Various tests were conducted to find the optimal ECG measurement position which has less noise and could get strong and clear ECG signal during exercise, walking, hiking, mountain climbing and cycling.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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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.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.285-288
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• 2008

In ubiquitous computing environment the biological signal ECG (Electrocardiogram signal) is usually recorded with noise components. Adaptive interference (or noise) canceller do adaptive filtering of the noise reference input to maximally match and subtract out noise or interference from the primary (signal plus noise) input thereby adaptively eliminate unwanted interference from the ECG signal. Measured Stress ECG (or exercise ECG signal) signal have three major noisy component like baseline wander noise, motion artifact noise and EMG (Electro-mayo-cardiogram) noise. These noises are not only distorted signal but also root of incorrect diagnosis while ECG data are analyzed. Motion artifact and EMG noises behave like wide band spectrum signals, and they considerably do overlapping with the ECG spectrum. Here the multidimensional adaptive method used for filtering which is more effective to improve signal to noise ratio.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.48-50
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• 2006

In this paper, we propose a method to minimize the baseline wandering that make hard to detect R wave in ECG. This method uses a different signal between ECG and ascending slope tracing waves to minimize the baseline wandering. When the slope of ECG signal maintains the value or falls, the ascending slope tracing wave fellows ECG signal directly, and this wave holds that value of ECG signal when the slope begins to rises in a certain time(=hold time). After this hold time, this wave traces ECG signal again. To evaluate this minimizing method for baseline wandering, the experiments are carried out with 5 ECG data in the database of MIT/BIH. R waves in the proposed different signal are detected by using descending slope trace waves and compared with the annotation file. The results show that the proposed method Is sure to minimize the baseline wandering in ECG.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2361-2376
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• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• 대한전자공학회논문지
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• 제26권9호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.325-327
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• 2004

ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.834-837
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• 2015

본 논문은 0-5 볼트의 범위에서 실시간 아날로그 ECG 신호를 생성하는 FPGA 하드웨어 기반 ECG 시뮬레이터를 설계하고 기능 및 특성을 기술하였다. 시뮬레이터에 의해 생성된 ECG 신호는 실험실 실험, 의료 기기 교정 및 연구에 사용되거나 다양하게 응용할 수 있다. 시뮬레이터의 ECG 신호는 신호 데이터를 생성하기 위하여 기존의 24bit 양자화로 획득한 후 실 데이터로 사용할 때에는 1kHz의 샘플링과 8비트로 분해, 양자화 되었다. 제안 시뮬레이터는 xilix Spartan-3를 이용하여 구현하였으며 PC와 설계 FPGA 시뮬레이터 간에 RS-232를 통하여 데이터를 전송할 수 있도록 하였다. 전송된 데이터는 메모리에 저장하고 저장된 데이터는 DAC(0808) 통하여 아날로그 ECG 신호로 출력되게 하였다. 또한 두 개의 버튼 수위를 통하여 심박수(HR)를 UP-DOWN할 수 있도록 하였다. 설계된 시스템의 평가를 위하여 기존의 심전도 입력을 사용하여 출력신호와 QRS파형의 획득을 위한 미분회로 통과 결과를 평가한 결과 적합한 결과를 얻을 수 있었다.

• 한국정보전자통신기술학회논문지
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• 제1권1호
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• pp.7-12
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• 2008

본 논문에서는 심전도(ECG)에서 R-파의 추출을 어렵게 하는 기저선 변동의 최소화 방법을 연구하였다. 이 방법에서는 기저선의 변동을 최소화하기 위하여 심전도와 상승 기울기 추적파의 차신호를 이용하였다. 여기서 상승 기울기 추적파는 심전도 신호에서 기울기가 하강할 때는 그대로 심전도 신호를 추적하고 기울기가 상승할 때는 그 시작시간의 심전도 신호를 기준값으로 일정시간 유지시켜 준 후 다시 원신호를 추적하는 파형이다. 구현된 방법의 효용성을 검증하기 위하여 MIT/BIH 데이터 베이스의 심전도에 적용하여 그 실효성을 확인하였다.