• 대한의용생체공학회:의공학회지
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• 제28권4호
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• pp.441-448
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• 2007

Heart rate variability (HRV) in electrocardiogram (ECG) is an important index for understanding the health status of heart and the autonomic nervous system. Most HRV analysis approaches are based on the proper heart rate (HR) data. Estimation of heart rate is thus a key process in the HRV study. In this paper, we report an innovative method to estimate the heart rate. This method is mainly based on the concept of periodicity transform (PT) and instantaneous period (IP) estimate. The method presented is accordingly called the "PT-IP method." It does not require ECG R-wave detection and thus possesses robust noise-immune capability. While the noise contamination, ECG time-varying morphology, and subjects' physiological variations make the R-wave detection a difficult task, this method can help us effectively estimate HR for medical research and clinical diagnosis. The results of estimating HR from empirical ECG data verify the efficacy and reliability of the proposed method.

• 센서학회지
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• 제20권6호
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• pp.382-387
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• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• 대한의용생체공학회:의공학회지
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• 제31권4호
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• pp.316-320
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• 2010

HRV(Heart rate variability) analysis parameter is widely used as an index to evaluate the autonomic nervous system and cardiac function. For reliable HRV analysis, we need to acquire the accurate ECG signals. Most of commercially available portable ECG devices have low sampling rate because of low power consumption and small size issues, which make it difficult to measure RR-interval accurately. This study is to improve the accuracy of RR-interval by developing R-wave interpolation technique, based on the morphological characteristics of the QRS complex. When the developed method was applied to ECG obtained at 200 Hz and the results were compared with 1000 Hz reference device, the error range decreased by 1.33 times in sitting and by 2.38 times in cycling exercise. Therefore, the proposed interpolation technique is thought to be useful to improve the accuracy of R-R interval in the portable ECG device with low sampling rate.

• 대한의용생체공학회:의공학회지
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• 제10권3호
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• pp.351-360
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• 1989

This paper describes the design of portable arrhythmia monitor and associated algorithm for automated diagnosis based-on microcomputer in the ambulatory ECG recording, analysis, and transmitting to a hospital host computer immediately through the telephone system. The device differs from Molter recorder in that it does not store normal ECG signals but captures and alarms the ECG during suspected abnormal periods and selected temporal epochs to a central hospital site. This porta file arrhythmia monitor makes use of a general purpose computer and software will be changed to meet the custom requirements of individual physicians and patients. At present it is very obvious that each cardiologist has his own method of analyzing ECG recordings and utilizes past experience more than the firm quantitative analysis of data.

• 한국산학기술학회논문지
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• 제15권4호
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• pp.2217-2224
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• 2014

본 논문은 의료용 아날로그 프론트 앤드(analog front end; AFE)를 이용하여 12채널 심전도를 측정하고 신호 분석을 통해 부정맥을 판단할 수 있는 시스템 개발에 관한 논문이다. 최근 국내 급성 심정지 발생이 증가하고 있으며 이에 원인이 되는 부정맥을 진단할 수 있는 시스템의 필요성이 증가하고 있다. 기존의 12채널 심전도 시스템은 회로 구성이 복잡하고 큰 부피를 차지하는 단점이 있으며 이를 개선하기 위해 본 논문에서는 의료용 AFE와 부정맥을 판단 할 수 있는 알고리즘 및 신호 처리를 위한 DSP로 시스템을 구성하였다. 추가적으로 12채널 심전도의 파형 관찰과 부정맥 판별 결과를 7인치 LCD를 통해 출력하며 터치 패널을 통해 사용자 입력을 받는다. 본 논문에서 구현된 시스템을 검증하기 위해 심전도 시뮬레이터의 출력 신호(정상 신호/부정맥 신호)에 대한 판별 테스트와 부정맥 알고리즘을 임베디드 환경에 적용하기 위한 최적화 과정의 성능 평가를 진행하였다.

• 전기학회논문지
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• 제56권7호
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• pp.1331-1338
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• 2007

A new method for detecting and eliminating the Electrocardiogram(ECG) artifact from the scalp Electroencephalogram(EEG) is proposed. Based on the single channel EEG, the proposed method consists of 4 procedures: emphasizing the R-wave of ECG artifact from EEG using the least squares acceleration(LSA) filter, detecting the R-wave from the LSA filtered EEG using the phase space method and R-R interval, generating the delayed impulse synchronized to the R-wave and elimination of the ECG artifacts based on the adaptive digital filter using the impulse and raw EEG. The performance of the proposed method was evaluated in the two separating parts of R-wave detection and, ECG estimation and elimination from EEG. In the R-wave detection, the proposed method showed the mean error rate of 6.285(%). In the ECG estimation and elimination using simulated and/or real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, in which independent component analysis and ensemble average method are used. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifact from single channel EEG and simple for ambulatory/portable EEG monitoring system.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.207-212
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• 1996

In this paper, we designed a Holter analysis system using the visual programming method. It differs from the existing analysis system in that the various signal processing algorithms represented by icons were designed by GUI concept which provide unskilled user with easy and convenient analysis environment. In order to analysis ECG signal, we only select the icon representing a algorithm to be applied by mouse and arrange the selected icons upon the order to be processed on screen. As a result it provides a convenient usage and flexibility of analysis. Finally, we can find the optimal algorithm for the ambulatory ECG analysis by comparing the several results obtained from the various analysis configuration.

• 디지털융복합연구
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• 제15권5호
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• pp.217-223
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• 2017

센서 네트워크와 IoT(Internet of Things)기술의 발달로, 개인의 상황정보를 수집하여 관련된 사용자들에게 맞춤화된 서비스의 제공이 가능해졌다. 현재, 상황정보 시스템은 사용자 특정 행동 특성을 분석하여 인지하고, 이벤트를 생성하는 수준에 있다. IoT 환경에서는 사람이 특정 목적을 갖고 행동하는 것이 아니라 의도하지 않아도 필요한 서비스를 사용자의 개입이 최소화되어 IoT 제품 스스로가 제공해주어야 한다. 본 논문에서는 IoT환경에서 사용자의 개입을 최소화하기 위해 비콘 기반에서 ECG를 이용해 상황정보를 이용한 자동출결 인지시스템을 구현했다. 특정 상황정보가 필요한 환경에서, 기존의 인증기법과 본 논문에서 제시한 ECG 센서를 이용한 인증 기법의 사용자 개입성을 비교 분석하였다. 분석 결과, 본 논문에서 구현한 시스템이 사용자의 개입을 최소화한다는 것을 확인했다.

• 동의신경정신과학회지
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• 제19권2호
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• pp.165-193
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• 2008

Objective: The ECG which used for this paper, is analysis result from alogrisms of arrythmia, and we have studied that how we could certain Cold(寒)type or Heat(熱) type and that Deficiency(虛) type or Excess(實)type of organs from various special diseases, and we obtained like these results. Method & Conclusion: 1. we depend on our examination about Pulse(脈) because we can't discriminate arrythmia using ECG analysis instruments. 2. We obtained that Cold(寒) type diseases had wave that prolonged above normal baseline and ST wave which had downward aptitude. 3. We obtained that Heat(熱) type diseases had the fibrillation which had shortend wave that compare to normal and had downward aptitude or negative aptitude. 4. We obtained that Respiratory system (肺) diseases had wave that is within normal or is short of normal range and had much fluctuation in potential difference or trans on P wave. 5. The character of EKG which presented about diseases of gastric systems is prolonged above narmal range of wave, and the ECG had represented mixed wave with Heat(熱) type when accompany inflammatory in gastric system. 6. The wave of Blood Stasis(瘀血) type had upward aptitude of QRS wave, and the wave of anemia or blood loss type(少血) had downward aptitude of QRS wave, the wave which had both Cold(寒)) and Heat (熱) represented mixed waves. 7. The Knotted Pulse(結脈) and Intermittent Pulse(代脈)is corresponded with sinus brady cardia, and the Swift Pulsle(疾脈) is corresponded with fibrillation. 8. We pay attention to the relations of formations about pressures pulse from formations of EMD( electromechanical dissociation ). Result : From these results, we will have to study about ECG which using in tests of change of Parkinsons disease.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.176-180
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• 2004

The ST-segment that the beginning part of T wave is the important diagnostic parameter to finding myocardial ischemia. Abnormal ST appears in two types. One is the level change, and the other is the pattern change. In this paper, we describe the monitoring of abnormal ST using PC based system. Hardware of this system consists of transmitter, receiver and PC. The function of transmitter is measuring ECG in three channels which are selected manually and transmitting the data to receiver by digital radio way. Connection with receiver and PC is by RS232C, and the data received on the PC is analyzed automatically by ECG analysis algorithm and saved to file. In the algorithm part for detecting abnormal ST, ST-segments are approximated by a polynomial. This method can detect all of the deviation and pattern change of ST-segment regardless the change in the heart rate or sampling rate. To gain algorithm reliability, the method rejects distorted polynomial approximation by calculation the difference between the approximated ST-segment and original ST-segment. In pre-signal processing, the wavelet transformation separates high frequency bands including QRS complex from the original ECG. Consequently, the process improves the performance of detecting each feature points.