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

• Main Authors: Soojeong Lee, Joon-Hyuk Chang
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Applied Sciences
• Subjects: oscillometric blood pressure estimation; deep Boltzman machine; machine learning; Dempster–Shafer fusion
• Online Access: http://www.mdpi.com/2076-3417/9/1/96

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.