• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.817-822
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• 1992

Distortion analysis of digital filters for the ECG signal processing is presented. Several band pass and band reject filters are designed for the analysis. Computer simulations are performed to compare the distortions of the Butterworth type filters and linear phase optimal fitters. The designed filters are applied to power line interference cancelling in ECG signals.

• 한국통신학회논문지
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• 제37권5C호
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• pp.403-409
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• 2012

This paper proposes an energy efficient ECG monitoring system by putting some intelligence on the sensor node to reduce the number of transmissions. The sensor node is mostly put into the processing mode and just connects the base station when necessary. Therefore, the transmission energy is greatly reduced while the energy for processing is increased a little bit. Our proposed ECG analysis method classifies ECG cycles by computing the Euclidean distance between the sensed ECG cycle and the reference ECG cycle. This work is a detailed and full explanation of our former work. Extended experimental results show that the proposed trade is very effective in saving energy and the Euclidean distance based classification method is accurate. Furthermore, the PowerTOSSIM energy simulation method is also demonstrated as very accurate in evaluating the energy consumption of the sensor node in our application scenario.

• 정보통신설비학회논문지
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• 제11권2호
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• pp.29-33
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• 2012

ECG widely used in cardiac function test is a graph that is recorded by measuring the electrical impulses occurred in the heart. Normal ECG has the form of similar sections that are repeated, and each section has the information occurred in a heart beat. Thus, In order to make the correct diagnosis, correct grasp of the sections and formed analysis must be done. In this research, a system that detects the sections of ECG is proposed. The system is based on ECG stored in the form of files. The ECG can easily have a noise caused by an outside factor. The noise of ECG is easily caused by external factors. Through a band-pass filter, it can be removed. and then, to get this ECG without a noise, interval detection algorithm using R-peak is applied. The clean, intuitive interface will help the above functions to be used without any difficulties.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권4호
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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.

• Journal of information and communication convergence engineering
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• 제5권2호
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• pp.98-103
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• 2007

Long term Electrocardiogram (ECG) [1] analysis plays a key role in heart disease analysis. A combined detection of QRS-complex and P-wave in ECG signal for ubiquitous healthcare system was designed and implemented which can be used as an advanced warning device. The ECG features are used to detect life-threating arrhythmias, with an emphasis on the software for analyzing QRS complex and P-wave in wireless ECG signals at server after receiving data from base station. Based on abnormal ECG activity, the server will transfer alarm conditions to a doctor's Personal Digital Assistant (PDA). Doctor can diagnose the patients who have survived from cardiac arrhythmia diseases.

• 대한의용생체공학회:의공학회지
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• 제29권4호
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• pp.278-285
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• 2008

In this study the complex-valued continuous wavelet transform (CWT) has been applied in detection of Electrocardiograms (ECG) as response to various signal classification methods such as Fourier transforms and other tools of time frequency analysis. Experiments have shown that CWT may serve as a detector of non-stationary signal changes as ECG. The tested signal is corrupted by short time events. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. Analysis of ECG signal using complex-valued continuous wavelet transform is the first step to detect possible changes and alternans. In the second step, modulus and phase must be thoroughly examined. Thus, short time events in the ECG signal, and other important characteristic points such as frequency overlapping, wave onsets/offsets extrema and discontinuities even inflection points are found to be detectable. We have proved that the complex-valued CWT can be used as a powerful detector in ECG signal analysis.

• 대한의용생체공학회:의공학회지
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• 제14권2호
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• pp.137-145
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• 1993

Time domain analysis as well as frequency domain analysis of signal conditioning filters is very useful for practical applications. Time domain analysis of digital filters for noise cancelling in ECG signals is presented. Several band pass and band reject filters are designed for the analysis. Computer simulations are performed to compare the distortions of the Butterworth type filters and linear phase optimal FIR filters which are widely used for ECG signal processing. Band reject filters are applied to power line interference cancelling in ECG signals.

• 한국통신학회논문지
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• 제35권9B호
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• 전기학회논문지
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• 제62권6호
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• pp.825-832
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• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

• 대한의용생체공학회:의공학회지
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• 제16권4호
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• pp.403-414
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• 1995

심전도 분석은 심장 전문의마다 기준이 다르고, 심전도 처리 시스템마다 측정된 변수 검출오차 때문에 많은 어려움이 있다. 이에 본 논문에서는 심전도 식별과정에서 발생하는 애매 모호성을 줄여주고, 불규칙한 심전도를 구간의 빈번도에 따라 통계학적으로 분석될 수 있도록 디지털 퍼지 프로세서를 사용한 STD-BUS용 실시간 심전도 신호 식별 시스템을 설계.제작하였다. 심전도를 판별하기 위해 사용된 변수는 나이, QRS폭, 평균 RRI, RRI등을 사용하였고, 이들 변수를 본 연구에서 제작한 심전도 신호 식별 시스템에 입력으로 사용한 결과, 일반 프로세서의 알고리즘에서 구별이 불가능했던 심전도 파형을 실시간으로 식별이 가능함을 확인할 수 있었다.