Improved Gaussian Mixture Regression Based on Pseudo Feature Generation Using Bootstrap in Blood Pressure Estimation
Soojeong Lee, Chee‐Hyun Park, Joon‐Hyuk Chang · IEEE Transactions on Industrial Informatics · 2015
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.