A pseudo-Bayesian model-based approach for noninvasive intracranial pressure estimation
Name
1046086564-MIT.pdf
Description
Full printable version
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12.98 MB
Format
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Checksum (MD5)
2709d71acb396726b27d67f65ffb4830
Author(s)
Imaduddin, Syed M. (Syed Muhammad)
Advisor(s)
Thomas Heldt.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
A noninvasive intracranial pressure (ICP) estimation method is proposed that incorporates model-based estimation within a probabilistic framework. A first-order subject-specific model of the cerebral vasculature relates arterial blood pressure with cerebral blood flow velocity. The model is solved for a range of physiologically plausible ICP values, and the resulting residual errors are transformed into likelihoods for each candidate ICP. The likelihoods are combined with a imulti-modal prior distribution of the ICP to yield an a posteriori distribution whose mode is taken as the final ICP estimate. An extension to this method is proposed to harness the temporal evolution of past ICP estimates for reducing dependence on the multi-modal prior distribution. This approach combines ICP estimates computed with a uniform prior belief with predictions from a single-state model of cerebral autoregulatory dynamics. This method was tested on data from thirteen patients from Boston Children's Hospital and yielded an ICP estimation bias (mean error or accuracy) of 0.3 nrmmHg and a root-mean-squared error (or precision) of 5.2 minHg. These performance characteristics are well within the acceptable range for clinical decision making. The method proposed here therefore constitutes a significant step towards robust, continuous, patient-specific noninvasive ICP determination.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 97-100).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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