Life after death : techniques for the prognostication of coma outcomes after cardiac arrest
Name
1052124083-MIT.pdf
Description
Full printable version
Size
15.1 MB
Format
Adobe PDF
Checksum (MD5)
42604dc536c1c94883e832db44cc3910
Author(s)
Ghassemi, Mohammad Mahdi
Advisor(s)
Roger G. Mark and Emery N. Brown.
Alternative Title
Techniques for the prognostication of coma outcomes after cardiac arrest
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Electroencephalography (EEG) features are known to predict neurological outcomes of patients in coma after cardiac arrest, but the association between EEG features and outcomes is time-dependent. Recent advances in machine learning allow temporally-dependent features to be learned from the EEG waveforms in a fully-automated way, allowing for faster, better-calibrated and more reliable prognostic predictions. In this thesis, we discuss three major contributions to the problem of coma prognostication after cardiac arrest: (1) the collection of the world's largest multi-center EEG database for patients in coma after cardiac arrest, (2) the development of time-dependent, interpretable, feature-based EEG models that may be used for both risk-scoring and decision support at the bedside, and (3) a careful comparison of the performance and utility of feature-based techniques to that of representation learning models that fully-automate the extraction of time-dependent features for outcome prognostication.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 120-134).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
