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
질문

논문 기본 정보

자료유형
학술저널
저자정보
Young-Seok Choi (Kwangwoon University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.6 No.5
발행연도
2017.10
수록면
334 - 340 (7page)
DOI
10.5573/IEIESPC.2017.6.5.334

이용수

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

초록· 키워드

오류제보하기
This paper proposes a new quantitative complexity measure of the electroencephalogram (EEG), which exploits intrinsic oscillatory features during brain injury and recovery after cardiac arrest. Considering the nonstationary nature of an EEG, a new complexity measure that incorporates the data-driven empirical mode decomposition method is developed, which decomposes a time-series into its intrinsic oscillatory components without the need of a pre-defined basis function. Therefore, the resulting entropy measure would reflect the irregularity of the inherent oscillatory components of the EEG, namely, intrinsic mode functions (IMFs). The Shannon entropy of the distributions of the IMFs in each intrinsic mode is evaluated. By averaging entropies in each intrinsic mode, a novel quantitative entropy measure called the intrinsic oscillation quantity (IOQ) is derived. Experimental EEG recordings from rats experiencing a sevenminute cardiac arrest followed by resuscitation were analyzed. Experimental results demonstrate that the proposed IOQ is able to discriminate the different injury levels and is highly correlated with an examined neurological deficit evaluation obtained after 72 hours, thus suggesting the prognostic potential of this measure.

목차

Abstract
1. Introduction
2. Intrinsic Oscillation Quantity
3. Experimental Method
4. Results
5. Conclusion
References

참고문헌 (23)

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