Key result
Oscillometric blood pressure estimation using a Gaussian mixture regression-based maximum amplitude algorithm yielded more accurate systolic and diastolic blood pressure values than the conventional method.
Answers are grounded in this paper and cite their sources.
A novel Gaussian mixture regression-based maximum amplitude algorithm improves the accuracy of oscillometric blood pressure estimation compared to conventional methods.
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.
No takes yet. Share an insight, caveat, or question.
Lee et al. (2013) studied Blood pressure estimation. Maximum amplitude algorithm (MAA) using Gaussian mixture regression (GMR) vs. Conventional MAA method was evaluated on Accuracy of SBP and DBP values. Oscillometric blood pressure estimation using a Gaussian mixture regression-based maximum amplitude algorithm yielded more accurate systolic and diastolic blood pressure values than the conventional method.
Synapse has enriched 10 related papers. Consider them for comparative context: