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Thomas Heldt. Matthews, Jonathan Martin Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2016-12-22T15:17:04Z 2016-12-22T15:17:04Z 2016 2016 http://hdl.handle.net/1721.1/105974 965641277 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 69-70). Monitoring of intracranial pressure (ICP) is key in many neurological conditions for diagnosis and guiding therapy. Current monitoring methods are highly invasive, limiting their use to the most critically ill patients. Based on a previously developed approach to noninvasive ICP estimation from cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) waveforms, I have implemented the algorithm on an embedded device (LPC4337 microcontroller) that can produce real-time estimates of ICP from noninvasively-obtained ABP and CBFV measurements. I have also fabricated a medical device prototype complete with peripheral interfaces for ABP and CBFV monitoring hardware and display and recording functionality for clinical use and post-acquisition analysis. The current device produces a mean estimate of ICP once per minute and can perform the necessary computations in 410 ms, on average. Real-time estimates of noninvasive ICP differed from the original batch-mode MATLAB implementation of the algorithm by 0.34 mmHg (RMSE). The contributions of this thesis take a step toward the goal of real-time noninvasive ICP estimation in a variety of clinical settings. by Jonathan Martin Matthews. M.Eng. 70 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. An embedded device for real-time noninvasive intracranial pressure estimation Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a23444a5-ba44-4eda-887d-f650a394f611"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>An embedded device for real-time noninvasive intracranial pressure estimation</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Matthews, Jonathan Martin</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Monitoring of intracranial pressure (ICP) is key in many neurological conditions for diagnosis and guiding therapy. Current monitoring methods are highly invasive, limiting their use to the most critically ill patients. Based on a previously developed approach to noninvasive ICP estimation from cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) waveforms, I have implemented the algorithm on an embedded device (LPC4337 microcontroller) that can produce real-time estimates of ICP from noninvasively-obtained ABP and CBFV measurements. I have also fabricated a medical device prototype complete with peripheral interfaces for ABP and CBFV monitoring hardware and display and recording functionality for clinical use and post-acquisition analysis. The current device produces a mean estimate of ICP once per minute and can perform the necessary computations in 410 ms, on average. Real-time estimates of noninvasive ICP differed from the original batch-mode MATLAB implementation of the algorithm by 0.34 mmHg (RMSE). The contributions of this thesis take a step toward the goal of real-time noninvasive ICP estimation in a variety of clinical settings.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>