Bayesian fusion algorithm for improved oscillometric blood pressure estimation. Issue 11 (November 2016)

• Record Type: Journal Article
• Authors: Forouzanfar, Mohamad; Dajani, Hilmi R.; Groza, Voicu Z.; Bolic, Miodrag; Rajan, Sreeraman; Batkin, Izmail
• Is Part Of: Medical engineering & physics. Volume 38:Issue 11(2016:Nov.)
• Page Start: 1300
• Page End: 1304
• Publication Date: 2016-11
• Subjects: Oscillometry -- Blood pressure estimation -- Fusion -- Bayesian estimator; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2016.08.003
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Oscillometric blood pressure estimation algorithms are fused for better performance. The goal is to exploit the advantages of the oscillometric algorithms. The goal is to circumvent the limitations of the oscillometric algorithms. A weighted combination of the algorithms estimates is computed using a Bayesian approach. Blood pressure estimation is improved relative to the individual algorithms. Abstract: A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively.