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Collin M. Stultz. Sridharan, Mathura J Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-03-06T15:46:56Z 2014-03-06T15:46:56Z 2013 http://hdl.handle.net/1721.1/85506 871003949 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. "May 24, 2013." Includes bibliographical references (pages 109-110). Identifying patients at high risk for adverse events is very important to the practice of clinical medicine. Non-invasive ECG-based methods of risk stratification such as T wave Alterans, Morphological Variability, and Heart Rate Variability extract prognostic information from the electrocardiograph. However, there is still a wealth of data collected from ICU patients and left unused every year that can augment risk-stratification methods. This thesis extends non-invasive risk stratification to Arterial Blood Pressure (ABP) Waveforms. We derive and analyze classifiers based on the morphological distance time series (derived from beat-to-beat morphology changes in the ABP waveform) including ASDNNmd, SDANNmd, rMSSDmd, the MVABP score etc. We also derive and analyze classifiers based on the Downstroke Time Series (derived from the decay from peak systole to diastole) including ASDNNDownstroke, SDANNDownstroke, rMSSDDownstroke, etc. While this body of work suggests the classifiers we developed are not effective in risk stratification of ICU patients, we discuss other methods which may extract prognostic information from the ABP waveform more effectively. by Mathura J. Sridharan. M.Eng. 110 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. Risk stratification of ICU patients using arterial blood pressure waveforms Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="afc59344-6b51-41ce-9804-0480e3559b02"> <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>Risk stratification of ICU patients using arterial blood pressure waveforms</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Sridharan, Mathura J</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>Identifying patients at high risk for adverse events is very important to the practice of clinical medicine. Non-invasive ECG-based methods of risk stratification such as T wave Alterans, Morphological Variability, and Heart Rate Variability extract prognostic information from the electrocardiograph. However, there is still a wealth of data collected from ICU patients and left unused every year that can augment risk-stratification methods. This thesis extends non-invasive risk stratification to Arterial Blood Pressure (ABP) Waveforms. We derive and analyze classifiers based on the morphological distance time series (derived from beat-to-beat morphology changes in the ABP waveform) including ASDNNmd, SDANNmd, rMSSDmd, the MVABP score etc. We also derive and analyze classifiers based on the Downstroke Time Series (derived from the decay from peak systole to diastole) including ASDNNDownstroke, SDANNDownstroke, rMSSDDownstroke, etc. While this body of work suggests the classifiers we developed are not effective in risk stratification of ICU patients, we discuss other methods which may extract prognostic information from the ABP waveform more effectively.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>