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심명헌(M. H. Sim),최한윤(H. Y. Choi),정인철(I. C. Jeong),김기원(K. W. Kim),윤형로(H. R. Yoon) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.10
The scalp is skin tissue for skull-protection and roots for hair growth. Therefore continuous monitoring of scalp condition is essential for hair management. However, the equipments for existent are inconvenient to use because of focus tremor and external factors(Hair Gel, Wax, accessories and so on). Furthermore there is a problem to use an expensive optical devices like CCD (Charge Coupled Device) camera or lens of 200~1000 magnification. It causes a difficulty of using those equipment. We design the special electrode(length 5.65㎜, diameter 0.8㎜ of needle shape) and the impedance system(1㎑, 78㎂). In this paper, we can measure scalp impedance with our system. Moreover, we find the possibility of classifying scalp condition with measured impedance values. For the classification of scalp condition, we used ARAMO-TS as an imaging system. In conclusion, the problem of existent devices could be improved using these method. It also has a benefit of continuous monitoring of scalp condition.
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웨이브렛 계수에 근거한 Fuzzy-ART 네트워크를 이용한 PVC 분류
박광리,이경중,이윤선,윤형로,Park, K. L,Lee, K. J.,lee, Y. S.,Yoon, H. R. 대한의용생체공학회 1999 의공학회지 Vol.20 No.4
본 연구에서는 PVC를 분류하기 위하여 웨이브렛 계수를 기반으로 하는 fuzzy-ART 네트워크를 설계하였다. 설계된 네트워크는 feature를 추출하는 부분과 fuzzy-ART 네트워크를 학습시키는 부분으로 구성된다. 우선 feature의 문턱치 구간을 설정하기 위하여 심전도 신호의 QRS를 검출하였고, 검출된 QRS는 Haar 웨이브렛을 이용한 웨이브렛 변환에 의해 주파수 분할하였다. 분할된 주파수 중에서 입력 feature를 추출하기 위하여 저주파 영역의 6번째 계수(D6)만을 선택하였다. D6신호는 입력 feature를 구성하기 위한 문턱치를 적용하여 fuzzy-ART 네트워크의 2진수 입력 feature로 전환하였고, PVC를 분류하기 위하여 fuzzy-ART네트워크를 학습시켰다. 본 연구의 성능을 평가하기 위하여 PVC가 포함된 MIT/BIH 데이터 베이스가 사용되었으며, fuzzy-ART 네트워크의 분류성능은 96.25%이었다. A fuzzy-ART(adaptive resonance theory) network for the PVC(premature ventricular contraction) classification using wavelet coefficient is designed. This network consists of the feature extraction and learning of the fuzzy-ART network. In the first step, we have detected the QRS from the ECG signal in order to set the threshold range for feature extraction and the detected QRS was divided into several frequency bands by wavelet transformation using Haar wavelet. Among the low-frequency bands, only the 6th coefficient(D6) are selected as the input feature. After that, the fuzzy-ART network for classification of the PVC is learned by using input feature which comprises of binary data converted by applying threshold to D6. The MIT/BIH database including the PVC is used for the evaluation. The designed fuzzy-ART network showed the PVC classification ratio of 96.52%.
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고재일,정인철,이동희,박신우,황성오,박소미,김기연,주현실,윤형로,Ko, J.I.,Jeong, I.C.,Lee, D.H.,Park, S.W.,Hwang, S.O.,Park, S.M.,Kim, G.Y.,Joo, H.S.,Yoon, H.R. 대한의용생체공학회 2007 의공학회지 Vol.28 No.2
This study is attempted to correct an error of electronic blood pressure meter with an optical sensor. In general, for a hospitalized patient, ECG, blood pressure, oxygen saturation, and respiration are basically measured to monitor the patient's condition. Opening of a blood vessel after it is occluded by pressurizing the cuff influences the blood flow of peripheral blood vessels as well as oscillation changes in the cuff. Blood vessels are occluded and peripheral blood flow disappears at cuff pressure above the examinee's blood pressure, while blood vessels are opened and peripheral blood flow appears again at cuff pressure under the examinee's blood pressure. Then Disappear-Appear Point Length(DAPL) of peripheral blood flow can be judged with the signal of peripheral blood flow, thus is available as a factor of error correction for electronic blood pressure meter. Also, systolic or diastolic blood pressure can be corrected with Appear-Point-Pressure(APP) of cuff pressure at a point where blood flow occurs and Appear-Maximum Pressure(AMP) of cuff pressure at the maximum amplitude point of peripheral blood flow after peripheral blood flow appears again. For verification, 27 examinees were selected, and their blood value was obtained through experimental procedure of 4 stages including induction of blood pressure change. The examinees were divided into two groups of experimental group and control group, regression analysis was conducted for experimental group, and correction of a blood pressure error was verified with optical signal by applying the regression equation calculated in experimental group to control group. As an experimental result, mean of the whole measurement errors was 5mmHg or more, which did not meet the standard fur blood pressure meter. As a result of correcting blood pressure measurements with data of DAPL, APP, and AMP as drawn out of PPG signal, systolic blood pressure, mean blood pressure, and diastolic blood pressure were $-0.6{\pm}4.4mmHg,\;-1.0{\pm}3.9mmHg$ and $-1.3{\pm}5.4mmHg$, respectively, indicating that mean of the whole measurement errors was greatly improved, and standard deviation was decreased.
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