Date of this Version
Radoslav Ivanov, James Weimer, Allan F. Simpao, Mohamed A. Rehman, and Insup Lee, "Early Detection of Critical Pulmonary Shunts in Infants", 6th International Conference on Cyber-Physical Systems (ICCPS 2015) , 110-119. April 2015. http://dx.doi.org/10.1145/2735960.2735962
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).
6th International Conference on Cyber-Physical Systems (ICCPS 2015)
© 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.
Time series analysis, Medical Information Systems
Date Posted: 04 May 2015
This document has been peer reviewed.