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A Non-Invasive Method for Estimating Cardiopulmonary Variables Using Breath-by-Breath Injection of Two Tracer Gases

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A Non-Invasive Method for Estimating Cardiopulmonary Variables Using Breath-by-Breath Injection of Two Tracer Gases

Conventional methods for estimating cardiopulmonary variables usually require complex gas analysers and the active co-operation of the patient. Therefore, they are not compatible with the crowded environment of the Intensive Care Unit (ICU) or operating theatre, where patient co-operation is typically impossible. However, it is these patients that would benefit the most from accurate estimation of cardiopulmonary variables, due to their critical condition.
This paper describes the results of a collaborative development between anaesthesiologists and biomedical engineers to create a compact and non-invasive system for the measurement of cardiopulmonary variables, such as lung volume, airway dead space volume, and pulmonary blood flow. In contrast with conventional methods, the compact apparatus and non-invasive nature of the proposed method allow it to be used in the ICU, as well as in general clinical settings.
We propose the use of a non-invasive method, in which tracer gases are injected into the patient’s inspired breath, and the concentration of the tracer gases is subsequently measured. A novel breath-by-breath tidal ventilation model is then used to estimate the value of a patient’s cardiopulmonary variables. Experimental results from an artificial lung demonstrate minimal error in the estimation of known parameters using the proposed method. Results from analysis of a cohort of 20 healthy volunteers (within the Oxford University Hospitals NHS Trust) show that the values of estimated cardiopulminary variables from these subjects lies within expected ranges. Advantages of this method are that it is non-invasive, compact, portable, and can perform analysis in real-time with less than one minute of acquired respiratory data.
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See complete bios of the authors in the full version of this article.
L CliftonL Clifton
Dr. Clifton is a post-doctoral research assistant at the Institute of Biomedical Engineering, in the Department of Engineering Science at the University of Oxford. Her research interests include the use of statistical machine learning for health informatics and physiological monitoring.

D CliftonD Clifton
Dr. Clifton is a Research Fellow at Mansfield College, Oxford and a College Lecturer at Balliol College, Oxford. His research interests are in statistical signal processing, particularly in biomedical informatics and other biomedical applications.

CEW HahnCEW Hahn
Dr. Hahn is Emeritus Professor of Anaesthetic Science in the Nuffield Department of Anaesthetics at the University of Oxford, and has retired as a Consultant in Clinical Measurement in the Oxford University Hospitals NHS Trust. His major research interest lies in the field of cardiopulmonary gas exchange in the sick and healthy lung.

AD FarmeryAD Farmery
Mr. Farmery is an academic physician-anaesthesiologist at the University of Oxford, and a Fellow and Tutor in Physiology at Wadham College, Oxford. His interests include biophotonics solutions to the measurement of dynamic biological signals.

Editorial Comments

The system proposed in this paper could be very useful in clinical practice, since implementation of assisted mechanical ventilation in Intensive Care Unit patients is very common and, up to now, cardiopulmonary ventilation parameters have been measured by expensive and complicated technology. Among the different solutions for noninvasive monitoring, the one described in this paper could be very promising for its compact and portable design and the ability to perform measurement after few minutes.

JTEHM 2013Issue

This article appeared in the 2013 issue of IEEE Journal of Translational Engineering in Health and Medicine.
View all articles on IEEE Xplore

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