Sensors 2013, 13(10), 13609-13623; doi:10.3390/s131013609
Article

On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation

1,* email, 2,* email and 3email
Received: 15 July 2013; in revised form: 2 September 2013 / Accepted: 24 September 2013 / Published: 10 October 2013
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The maximum amplitude algorithm (MAA) is generally utilized in the estimation of the pressure values, and it uses heuristically obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) in order to estimate the systolic and diastolic pressures. This paper proposes a Bayesian model to estimate the systolic and diastolic ratios. These ratios are an improvement over the single fixed systolic and diastolic ratios used in the algorithms that are available in the literature. The proposed method shows lower mean difference (MD) with standard deviation (SD) compared to the MAA for both SBP and DBP consistently in all the five measurements.
Keywords: oscillometric blood pressure estimation; systolic and diastolic ratios; Bayesian model; maximum amplitude algorithm
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MDPI and ACS Style

Lee, S.; Jeon, G.; Lee, G. On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation. Sensors 2013, 13, 13609-13623.

AMA Style

Lee S, Jeon G, Lee G. On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation. Sensors. 2013; 13(10):13609-13623.

Chicago/Turabian Style

Lee, Soojeong; Jeon, Gwanggil; Lee, Gangseong. 2013. "On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation." Sensors 13, no. 10: 13609-13623.

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