Title :
Oscillometric Blood Pressure Estimation Based on Maximum Amplitude Algorithm Employing Gaussian Mixture Regression
Author :
Soojeong Lee ; Joon-Hyuk Chang ; Sang Won Nam ; Chungsoo Lim ; Rajan, Sreeraman ; Dajani, Hilmi R. ; Groza, Voicu Z.
Author_Institution :
Dept. of Electron. Eng., Hanyang Univ., Seoul, South Korea
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.
Keywords :
Gaussian processes; blood pressure measurement; estimation theory; feature extraction; medical signal processing; patient diagnosis; regression analysis; signal classification; DBP value; DBPR discrimination; DBPR estimation; GMR method; Gaussian mixture model; MAA method; SBP value; SBPR discrimination; SBPR estimation; diastolic blood pressure ratio estimation; feature vector selection; gaussian mixture regression; maximum amplitude algorithm; oscillometric blood pressure estimation; systolic blood pressure ratio estimation; Accuracy; Biomedical monitoring; Blood pressure; Estimation; Gaussian mixture model; Standards; Gaussian mixture regression (GMR); maximum amplitude algorithm (MAA); oscillometric blood pressure estimation;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2013.2273612