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3차원 심실모델을 이용한 심장의 활성화 과정에 대한 시뮬레이션 연구
이경중,박금수,윤형로,Lee, Kyoung-Joung,Park, Gum-Soo,Yun, Hyung-Ro 대한의용생체공학회 1992 의공학회지 Vol.13 No.2
The cardiac activation process uslng three dimensional ventricular model is simulated. To study this theme, we constructed a cardiac ventricular model and simulated the cardiac activation process using the action potential duration and the activation time. The cardiac ventricular model is generated by the loglcal combination of the elliptic equations. The action potential duration could be obtained from the fact that It Is linearly distributed between model cells. The cardiac activation process was simulated by the law of "all-or-none". Based on the activation time and the action potential duration the cardiac potential at the arbitrary time after the activation of the model cell was computed. To test the validity of model, the comparison of the results of model simulation with the physiological data was performed. In conclusion, this model shows the simular results which is comparable to the 1 Pal conduction of the cardlac excitation.xcitation.
시계열 자료의 데이터마이닝을 위한 패턴분류 모델설계 및 성능비교
이수용(Lee Soo Yong),이경중(Lee Kyoung Joung) 한국지능시스템학회 2011 한국지능시스템학회논문지 Vol.21 No.6
본 연구는 순차적인 시계열 자료들에서 가장 최근의 추세가 반영될 수 있는 패턴분류 모델을 설계하였다. 의사결정을 지원하는 데이터마이닝 패턴분류 모델을 설계할 때 통계 기법과 인공지능 기법을 융합한 모델들이 기존의 모델보다 우수함을 입증하였다. 특히 퍼지이론과 융합된 패턴분류 모델들의 적중률이 상대적으로 더 향상되었다. 예를 들어, 통계적 이론을 기반으로 한 SVM모델과 퍼지소속함수와의 결합, 혹은 신경망과 FCM을 결합한 모델들의 성능이 우수하였다. 실험에서 사용한 패턴분류 모델들은 BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, Regression Analysis 등이다. 그리고 데이터베이스는 시계열 속성을 지닌 금융시장의 경제지표 DB(한국, KDSPI200 데이터베이스)와 병원 응급실의 부정맥환자에 대한 심전도 DB(미국 MIT-BIH 데이터베이스)들을 사용하였다. In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market (Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies (USA, MIT-BIH DB) were used for data base.
장봉렬,박호동,이경중,Jang, Bong-Ryeol,Park, Ho-Dong,Lee, Kyoung-Joung 대한의용생체공학회 2007 의공학회지 Vol.28 No.1
MR(magnetic resonance) image of moving organ such as heart shows serious distortion of MR image due to motion itself. To eliminate motion artifacts, MRI(magnetic resonance imaging) scan sequences requires a trigger pulse like ECG(electro-cardiography) R-wave. ECG-gating using cardiac cycle synchronizes the MRI sequence acquisition to the R-wave in order to eliminate image motion artifacts. In this paper, we designed ECG/PPG(photo-plethysmography) gating system which is for eliminating motion artifacts due to moving organ. This system uses nonmagnetic carbon electrodes, lead wire and shield case for minimizing RF(radio-frequency) pulse and gradient effect. Also, we developed a ECG circuit for preventing saturation by magnetic field and a finger plethysmography sensor using optic fiber. And then, gating pulse is generated by adaptive filtering based on NLMS(normalized least mean square) algorithm. To evaluate the developed system, we measured and compared MR imaging of heart and neck with and without ECG/PPG gating system. As a result, we could get a clean image to be used in clinically. In conclusion, the designed ECG/PPG gating system could be useful method when we get MR imaging of moving organ like a heart.
오실로메트릭 혈압 측정에서 커패시턴스 센서와 적응필터를 이용한 새로운 잡음제거방법에 관한 연구
최현석,박호동,이경중,Choi, Hyun-Seok,Park, Ho-Dong,Lee, Kyoung-Joung 대한의용생체공학회 2008 의공학회지 Vol.29 No.3
In this study, a new method using a capacitive sensor and an adaptive filter was proposed to deal with artifacts contaminating an oscillation signal in oscilometric blood pressure measurement. The proposed method makes use of a variation of the capacitance between an electrode fixed to a cuff and an external object to detect artifacts caused by the external object bumping into the cuff. The proposed method utilizes the adaptive filter based on linear prediction to remove the detected artifacts. The conventional method using linear interpolation and the proposed method using the adaptive filter were applied to three types of the artifact-contaminated oscillation signals(no overlap, non-consecutive overlap, and consecutive overlap between artifacts and oscillations) to compare them in terms of the artifact reduction performance. The proposed method was more robust than the conventional method in the case of consecutive overlap between artifacts and oscillations. The proposed method could be useful for measuring blood pressure in such a noisy environment that the subject is being transported.
영아 돌연사 방지를 위한 비접촉 방식의 가정용 영아 호흡 감시 시스템 개발
허일강,명현석,이경중,Heo, Il-Kang,Myoung, Hyoun-Seok,Lee, Kyoung-Joung 대한의용생체공학회 2015 의공학회지 Vol.36 No.2
Sudden infant death syndrome(SIDS) continues to be general cause of infant death. Also, apnea is supposed to be one of the main risk factor of SIDS. Therefore, Infant's respiratory monitoring and real-time apnea detection is very important to prevent SIDS. In this study, we proposed a non-contact home monitoring system for infant's respiration using Doppler radar in order to prevent SIDS. The respiration data were acquired from a commercialized baby simulator(Simbaby$^{TM}$) using a Doppler radar. To evaluate a performance of the proposed system, the simulator was placed in a supine and prone position and the chest belt was used simultaneously as a reference signal. As a result, correlation coefficients between respiration rates of Doppler radar and the chest belt in each position were 0.95(p < 0.001) and 0.98(p < 0.001), respectively. The averages of difference were $-0.29{\pm}5.21(mean{\pm}1.96{\cdot}$ standard deviation) in supine and $-0.12{\pm}3.05$ in prone from Bland-Altman analysis. The results indicated an excellent performance in detecting apnea with a sensitivity of 100% and a positive predictive value of 100% in each posture respectively. These results demonstrated that a proposed Doppler radar system is suitable for non-contact respiratory monitoring in order to prevent SIDS of infant.