메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
권혁찬 (Biomagnetism Research Center, Korea Research Institute of Standards and Science) 김기웅 (Biomagnetism Research Center, Korea Research Institute of Standards and Science) 김진목 (Biomagnetism Research Center, Korea Research Institute of Standards and Science) 이용호 (Biomagnetism Research Center, Korea Research Institute of Standards and Science) 김태은 (Biomagnetism Research Center, Korea Research Institute of Standards and Science) 임현균 (Biomagnetism Research Center, Korea Research Institute of Standards and Science) 고영국 (Cardiovascular Center, College of Medicine, Yonsei University) 정남식 (Cardiovascular Center, College of Medicine, Yonsei University)
저널정보
한국초전도학회 Progress in superconductivity Progress in superconductivity 제7권 제1호
발행연도
2005.1
수록면
41 - 45 (5page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
The diagnostic management of patients with chest pain remains a clinical challenge. Magnetocardiography (MCG) has been proposed as a new non-invasive method for detection of myocardial ischemia. To date, however, MCG technique is not intensively introduced for clinical use. One of the main reasons might be the absence of statistically valid and diagnostically clean criteria, which can determine the presence of certain heart disease. In this work, we suggested a new method to classify the diagnostic value of MCG for the detection of coronary artery disease (CAD) in patients with chest pain. MCG was recorded for three groups (healthy subjects and patients without and with CAD) by means of the 64 channel SQUID gradiometer system installed at a hospital. Using four parameters, which were found to be significantly different between groups, we evaluated a probability, in which parameters can be classified into each group based on the distribution function of the parameter in each group. For all parameters, sum of probabilities was compared between groups to determine the presence of CAD. Our classification method shows that the MCG can be a useful tool to predict the presence of CAD with sensitivity and specificity of higher than $80\%$ each.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0