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Cited 32 time in webofscience Cited 36 time in scopus
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Oscillometric Blood Pressure Estimation Based on Maximum Amplitude Algorithm Employing Gaussian Mixture Regression

Authors
Lee, SoojeongChang, Joon-HyukNam, Sang WonLim, ChungsooRajan, SreeramanDajani, Hilmi R.Groza, Voicu Z.
Issue Date
Dec-2013
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Gaussian mixture regression (GMR); maximum amplitude algorithm (MAA); oscillometric blood pressure estimation
Citation
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, v.62, no.12, pp.3387 - 3389
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume
62
Number
12
Start Page
3387
End Page
3389
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/26590
DOI
10.1109/TIM.2013.2273612
ISSN
0018-9456
Abstract
This paper introduces a novel approach to estimate the systolic and diastolic blood pressure ratios (SBPR and DBPR) based on the maximum amplitude algorithm (MAA) using a Gaussian mixture regression (GMR). The relevant features, which clearly discriminate the SBPR and DBPR according to the targeted groups, are selected in a feature vector. The selected feature vector is then represented by the Gaussian mixture model. The SBPR and DBPR are subsequently obtained with the help of the GMR and then mapped back to SBP and DBP values that are more accurate than those obtained with the conventional MAA method.
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서울 공과대학 (서울 융합전자공학부)
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