Development and analysis of a simple grey-box model of central sleep apnea
Name
862112155-MIT.pdf
Description
Full printable version
Size
10.52 MB
Format
Adobe PDF
Checksum (MD5)
21b275204306076f242468d9eea2a1d3
Author(s)
Kazerani, Ali
Advisor(s)
John N. Tsitsiklis and George C. Verghese.
Alternative Title
Grey-box model of central sleep apnea
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we develop and analyze a simple grey-box model that describes the pathophysiology of central sleep apnea (CSA). We construct our model following a thorough survey of published approaches. Special attention is given to PNEUMA, a complex, comprehensive model of human respiratory and cardiovascular physiology that brings together many existing physiological models. We perform a sensitivity analysis, concluding that signals of interest in PNEUMA are insensitive to changes in all but approximately twenty parameters. This justifies our goal of developing a small, simple model that captures approximately the same behaviour among signals of interest. The simplicity of our model not only makes it accessible to analytical and intuitive exploration, but also opens up the possibility that its parameters could be reliably estimated from a patient's data records. This could be of great value in developing patient-specific or state-specific treatments for CSA. Our model describes the dynamics of the alveolar gas exchange, blood gas transport, and cerebral gas exchange processes, which together determine the cerebral and arterial partial pressures of carbon dioxide, given ventilation as input. Our model of the ventilatory controller senses both the cerebral and arterial carbon dioxide partial pressures and issues a ventilatory drive command from which the level of ventilation is determined, closing the loop. We develop a linearized small-signal model of our system and determine conditions for its stability. We conclude by comparing the stability predictions suggested by our linear analysis to the stability properties of our original nonlinear model, with promising results.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 125-126).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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