Oscillometric Blood Pressure Estimation Based on Maximum Amplitude Algorithm Employing Gaussian Mixture Regression
- Authors
- Lee, Soojeong; Chang, Joon-Hyuk; Nam, Sang Won; Lim, Chungsoo; Rajan, Sreeraman; Dajani, 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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