• Journal of Multimedia Information System
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• 제3권2호
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1989년도 춘계학술대회
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• pp.13-14
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• 1989

Prolongation in QT corrected interval (QTc), measured in surface ECG, has been shown in the majority af patients to be marker of bad prognosis in postmyocardial infarction patients (PMIP). Hence it would seem logical that dynamic QTc interval measurement can be a very usefull indicator to stratify prognosis in PMIP. We present a new algorithm for QT as well as for QTP (distance value from Q wave onset to T wave peak) intervals measurement in 24 hour ECG Holter tapes. Validation of the algorithm by hand measurement has been done on first beats of 18 Holter tapes, resulting in a magnitude of deviations between 10 and 15 ms. Application on 24 hour Holter ECG signal has also been done.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2004년도 춘계학술대회
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• pp.241-245
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• 2004

In this paper, we implement a portable bioelectric signal measurement system for the safety of industrial workers. The developed system consists of two parts: the one is boielectric signal measurement unit and the other is signal analyzer system with PDA. The former includes signal processing part, A/D convertor, and 8051 based microprocessor, the latter includes software for signal analysis and display. The developed system detects industrial worker's ECG and displays and stores it to PDA. The ECG data in PDA can be transmitted to PC located in a distance, allowing a doctor to review the ECG and make a treatment decision. A doctor analyzes the ECG data and gives medical treatment to industrial worker.

• 센서학회지
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• 제28권1호
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 추계학술발표대회
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• pp.1399-1402
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• 2011

본 논문에서는 조기 심실 수축과 조기 심방 수축을 검출함에 있어 정밀한 QRS 구간의 폭, 정확한 P파와 T파의 크기 및 위치를 크게 요구하지 않고, 데이터의 가공과 복잡한 알고리즘의 사용에 의해 발생하는 ECG 데이터의 변형과 손실을 최소화할 수 있으며, 또한 개인차 때문에 발생할 수 있는 오류를 최소화하기 위한 알고리즘을 제안한다. 이를 위해 ECG 신호를 각각의 단위 파형으로 분리한 후, 정상 R-R 간격을 가지는 파형을 기준으로 기준파형을 만들어, 각 파형과 기준파형사이의 패턴 대조 및 유사도 분석을 통해 조기 심실수축과 조기심방수축을 검출할 수 있도록 하였다.

• 융합신호처리학회논문지
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• 제22권2호
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• pp.45-50
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• 2021

최근 IT기술이 발달함에 따라 다양한 생체신호 측정 기기에 대한 연구 및 관심이 높아지고 있으나, 가장 대표적인 생체 신호 중 하나인 심전도, 특히 부정맥 신호 검출과 관련한 연구는 미비한 현실이다. 부정맥은 그 발병원인이 다양하며 발병이후 예후가 좋지 않으므로 조기진단을 통한 예방치료가 최선이다. 하지만 부정맥을 진단하기 위한 도구인 24시간 홀터 심전계는 사용지속시간의 제약, 일상생활로 인한 동잡음 분석의 어려움, 위험상황에서 사용자의 실시간 알람 기능에 단점을 보인다. 본 연구에서는 장시간 연속 측정이 가능한 심전도 및 맥박 모니터링 기기와 실시간 모니터링 앱, 분석용 소프트웨어를 개발하였으며, 측정한 값의 경향성을 확인하였다. 향후 연구에서는 심전도 신호 측정 분석의 정량적 결과 도출에 관한 연구가 필요하며, 이를 바탕으로 하는 부정맥 신호 검출 알고리즘 개발과 관련한 추가 연구를 진행해야 한다.

• 한국식품영양과학회지
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• 제21권4호
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• pp.386-389
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• 1992

녹차에 있어서 맛성분으로도 중요하고, 최근에는 그 약리작용의 연구과 활발히 연구되고 있는 catechin의 함량 분석을 국산 시판 녹차를 시료로 하여 시도하였다. 100mg의 시료 분말 녹차를 열수 출출한 후 $CHCl_3$를 가하여 시료 용액으로 부터 caffeine를 제거시키고 EtOAc를 가하여 catechin류를 추출 농축하여 5ml로 한 다음 $0.45\;\mu\textrm{m}$ membrane filter를 통과시킨 후 $2\;\mu\textrm{l}$을 HPLC에 주입시켰다. 4종류의 고순도 정량분석용 catechin류를 표준용액으로 한 검량선법에 의하여 시료중의 catechin 함량을 구하였다. 그 결과, EGCg의 함량(찐 1번차 : 7.54%, 볶은 1번차 : 7.88%)이 가장 많았고 EGC, ECg, EC의 순서로 그 함량이 감소하엿다. 수확시기가 늦은 2번차에서는 EGCg, ECg 와 같은 gallate는 증가하고 EGC는 약간 감소하는 경향을 나타내어 차의 맛에 영향을 주는것 같다.

• 한국의류학회지
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• 제45권2호
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• pp.305-316
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• 2021

In this study, the effect of wearing functional compression pants is verified using a lower-limb wearable robot through a bio-signal analysis and subjective fit evaluation. First, the compression area to be applied to the functional compression pants is derived using the quad method for nine men in their 20s. Subsequently, functional compression pants are prepared, and changes in Electroencephalogram (EEG) and Electrocardiogram (ECG) signals when wearing the functional compression and normal regular pants inside a wearable robot are measured. The EEG and ECG signals are measured with eyes closed and open. Results indicate that the Relative alpha (RA) and Relative gamma wave (RG) of the EEG signal differ significantly, resulting in increased stability and reduced anxiety and stress when wearing the functional compression pants. Furthermore, the ECG analysis results indicate statistically significant differences in the Low frequency (LF)/High frequency (HF) index, which reflect the overall balance of the autonomic nervous system and can be interpreted as feeling comfortable and balanced when wearing the functional compression pants. Moreover, subjective sense is discovered to be effective in assessing wear fit, ease of movement, skin friction, and wear comfort when wearing the functional compression pants.

• Journal of Biosystems Engineering
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• 제33권5호
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• pp.317-325
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• 2008

The electrocardiogram (ECG) measurement system consists of I/O interface to input the ECG signals from two electrodes, FPGA (Field programmable gate arrays) module to process the signal conditioning, and real time module to control the system. The algorithms based on wavelet transform were developed to remove the noise of the ECG signals and to determine the QRS-waves. Triangular wave tests were conducted to determine the optimal factors of the wavelet filter by analyzing the SNRs (signal to noise ratios) and RMSEs (root mean square errors). The hybrid rule, soft method, and symlets of order 5 were selected as thresholding rule, thresholding method, and mother wavelet, respectively. The developed wavelet filter showed good performance to remove the noise of the triangular waves with 10.98 dB of SNR and 0.140 mV of RMSE. The ECG signals from a total of 6 subjects were measured at different measuring postures such as lying, sitting, and standing. The durations of QRS-waves, the amplitudes of R-waves, the intervals of RR-waves were analyzed by using the finite impulse response (FIR) filter and the developed wavelet filter. The wavelet filter showed good performance to determine the features of QRS-waves, but the FIR filter had some problems to detect the peaks of Q and S waves. The measuring postures affected accuracy and precision of the ECG signals. The noises of the ECG signals were increased due to the movement of the subject during measurement. The results showed that the wavelet filter was a useful tool to remove the noise of the ECG signals and to determine the features of the QRS-waves.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.