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논문 기본 정보

자료유형
학술저널
저자정보
Kwon-Ha Yoon (Wonkwang University) Yunyoung Nam (Soonchunhyang University) Tharoeun Thap (Wonkwang University) Changwon Jeong (Wonkwang University) Nam Ho Kim (Wonkwang University) Joem Seok Ko (Wonkwang University) Se-Eung Noh (Wonkwang University) Jinseok Lee (Wonkwang University)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.12 No.1
발행연도
2017.1
수록면
346 - 355 (10page)

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초록· 키워드

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Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n =23), the MIT-BIH Normal Sinus Rhythm (n =18), the BIDMC Congestive Heart Failure (n =13) and the Congestive Heart Failure RRI databases (n =25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n =42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

목차

Abstract
1. Introduction
2. Materials and Methods
3. Results
4. Discussions and Conclusions
References

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UCI(KEPA) : I410-ECN-0101-2017-560-002013636