Departmental Papers (CIS)

Date of this Version

4-14-2015

Document Type

Conference Paper

Comments

6th International Conference on Cyber-Physical Systems (ICCPS 2015) http://iccps.acm.org/2015/ in conjunction with CPS Week 2015 http://www.cpsweek.org/2015/ , Seattle, WA, April 14-16, 2015

Abstract

This paper aims to improve the design of modern Medical Cyber Physical Systems through the addition of supplemental noninvasive monitors. Specifically, we focus on monitoring the arterial blood oxygen content (CaO2), one of the most closely observed vital signs in operating rooms, currently measured by a proxy - peripheral hemoglobin oxygen saturation (SpO2). While SpO2 is a good estimate of O2 content in the finger where it is measured, it is a delayed measure of its content in the arteries. In addition, it does not incorporate system dynamics and is a poor predictor of future CaO2 values. Therefore, as a first step towards supplementing the usage of SpO2, this work introduces a predictive monitor designed to provide early detection of critical drops in CaO2 caused by a pulmonary shunt in infants.

To this end, we develop a formal model of the circulation of oxygen and carbon dioxide in the body, characterized by unknown patient-unique parameters. Employing the model, we design a matched subspace detector to provide a near constant false alarm rate invariant to these parameters and modeling uncertainties. Finally, we validate our approach on real-patient data from lung lobectomy surgeries performed at the Children's Hospital of Philadelphia. Given 198 infants, the detector predicted 81% of the critical drops in CaO2 at an average of about 65 seconds earlier than the SpO2-based monitor, while achieving a 0:9% false alarm rate (representing about 2 false alarms per hour).

Subject Area

CPS Medical

Publication Source

6th International Conference on Cyber-Physical Systems (ICCPS 2015)

Start Page

110

Last Page

119

DOI

10.1145/2735960.2735962

Copyright/Permission Statement

© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, http://dx.doi.org/10.1145/2735960.2735962.

Keywords

Time series analysis, Medical Information Systems

Share

COinS
 

Date Posted: 04 May 2015

This document has been peer reviewed.