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Improved Gaussian Mixture Regression Based on Pseudo Feature Generation Using Bootstrap in Blood Pressure Estimation

Title
Improved Gaussian Mixture Regression Based on Pseudo Feature Generation Using Bootstrap in Blood Pressure Estimation
Author
장준혁
Keywords
Blood pressure; bootstrap; Gaussian mixture model (GMM); Gaussian mixture regression (GMR); GMM-based clustering; k-means clustering; oscillometric blood pressure estimation
Issue Date
2016-12
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v. 12, Issue 6, Page. 2269-2280
Abstract
Although the systolic and diastolic blood pressure ratios (SBPRs and DBPRs) based on the conventional maximum amplitude algorithm (MAA) are assumed to be fixed; this assumption is not valid. In this paper, we present an improved Gaussian mixture regression (IGMR)approach that can accurately measure blood pressure. The SBPR and DBPR are estimated by using the IGMR technique. Specifically, the number of feature’s samples in the clustered feature space is increased using the nonparametric bootstrap technique to create the pseudo feature. The pseudo feature vector is much more matched than the original feature for the Gaussian mixture model(GMM) to fit individual BP characteristics in the training stage. By using the classified targeting clusters, we eventually estimate the SBPR and DBPR based on the IGMR technique at the test stage. The mean error (ME) and standard deviation of the error (SDE), and mean absolute error (MAE) of the SBP and DBP estimates obtained with the SBPR and DBPR using the proposed technique approaches are superior to the ME, SDE, and MAE of the estimates obtained using the conventional methods. The difference in the SDE between the proposed technique and the conventional MAA technique for the SBP and DBP turned out to be 3.67 and 3.08 mmHg in the simulation.
URI
https://ieeexplore.ieee.org/document/7286830http://repository.hanyang.ac.kr/handle/20.500.11754/101775
ISSN
1551-3203; 1941-0050
DOI
10.1109/TII.2015.2484278
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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