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Risk stratification by analysis of electrocardiographic morphology following acute coronary syndromes

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dc.contributor.advisor John V. Guttag. en_US
dc.contributor.author Sung, Philip Pohong en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2010-03-24T20:35:47Z
dc.date.available 2010-03-24T20:35:47Z
dc.date.copyright 2009 en_US
dc.date.issued 2009 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/52769
dc.description Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Includes bibliographical references (p. 95-100). en_US
dc.description.abstract Patients who have suffered an acute coronary syndrome (ACS) are at elevated risk of future adverse events, including fatal arrhythmias or myocardial infarction. Risk stratification--he identification of high-risk patients--s an important step in determining who is most likely to benefit from aggressive treatments. We propose a new automated risk stratification technique that uses the long-term electrocardiographic data routinely recorded in the days following an ACS. Data obtained from clinical drug trials indicates that our technique, called MV-DF (morphologic variability diagnostic frequencies), can significantly improve prognostication for ACS patients. Patients with MV-DF values in the highest quartile show a more than five-fold elevated risk of death in the 90 days following a non-ST-elevation ACS. We also propose techniques to construct models of the dynamics of cardiac behavior. Preliminary results suggest that such techniques may be useful for short-term prediction of fatal arrhythmias. Our results suggest that long-term ECG-based risk assessment techniques--n particular, methods incorporating information about morphologic variability--re an effective and practical way to select appropriate treatment options for cardiovascular disease patients. en_US
dc.description.statementofresponsibility by Philip Pohong Sung. en_US
dc.format.extent 100 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Risk stratification by analysis of electrocardiographic morphology following acute coronary syndromes en_US
dc.type Thesis en_US
dc.description.degree M.Eng. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 505525431 en_US


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