Estimation of Blood Oxygen Content Using Context-Aware Filtering

Radoslav Ivanov, Nikolay Atanasov, James Weimer, Miroslav Pajic, Allan Simpao, Mohamed Rehman, George Pappas, Insup Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we address the problem of estimating the blood oxygen concentration in children during surgery. Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real- patient data collected over the last decade at the Children's Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables.

Original languageEnglish (US)
Title of host publication2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems, ICCPS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509017720
DOIs
StatePublished - May 25 2016
Event7th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2016 - Vienna, Austria
Duration: Apr 11 2016Apr 14 2016

Publication series

Name2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems, ICCPS 2016 - Proceedings

Other

Other7th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2016
CountryAustria
CityVienna
Period4/11/164/14/16

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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