Next Article in Journal
Observation of the Velocity Variation of an Explosively-Driven Flat Flyer Depending on the Flyer Width
Previous Article in Journal
Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal
Open AccessArticle

Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation

Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(1), 96; https://doi.org/10.3390/app9010096
Received: 10 November 2018 / Revised: 13 December 2018 / Accepted: 20 December 2018 / Published: 28 December 2018
(This article belongs to the Section Applied Biosciences and Bioengineering)
We propose a technique using Dempster–Shafer fusion based on a deep Boltzmann machine to classify and estimate systolic blood pressure and diastolic blood pressure categories using oscillometric blood pressure measurements. The deep Boltzmann machine is a state-of-the-art technology in which multiple restricted Boltzmann machines are accumulated. Unlike deep belief networks, each unit in the middle layer of the deep Boltzmann machine obtain information up and down to prevent uncertainty at the inference step. Dempster–Shafer fusion can be incorporated to enable combined independent estimation of the observations, and a confidence increase for a given deep Boltzmann machine estimate can be clearly observed. Our work provides an accurate blood pressure estimate, a blood pressure category with upper and lower bounds, and a solution that can reduce estimation uncertainty. This study is one of the first to use deep Boltzmann machine-based Dempster–Shafer fusion to classify and estimate blood pressure. View Full-Text
Keywords: oscillometric blood pressure estimation; deep Boltzman machine; machine learning; Dempster–Shafer fusion oscillometric blood pressure estimation; deep Boltzman machine; machine learning; Dempster–Shafer fusion
Show Figures

Figure 1

MDPI and ACS Style

Lee, S.; Chang, J.-H. Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation. Appl. Sci. 2019, 9, 96.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop