Background: Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients. Methods: This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities. Results: The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE. Conclusions: The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates.

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Keywords Blood pressure measurement, Bootstrap technique, Confidence interval, Gaussian mixture regression, Machine learning, Oscillometric method
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Journal Computers in Biology and Medicine
Lee, S. (Soojeong), Rajan, S, Jeon, G. (Gwanggil), Chang, J.-H. (Joon-Hyuk), Dajani, H.R. (Hilmi R.), & Groza, V.Z. (Voicu Z.). (2017). Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model. Computers in Biology and Medicine, 85, 112–124. doi:10.1016/j.compbiomed.2015.11.008