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Chi-Sang Poon. Arzeno, Natalia M. (Natalia María Arzeno Soltero) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2008-04-23T14:35:50Z 2008-04-23T14:35:50Z 2007 2007 http://hdl.handle.net/1721.1/41252 213391321 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. Includes bibliographical references (p. 103-109). This thesis examines two challenging problems in chaos analysis: distinguishing deterministic chaos and stochastic (noise-induced) chaos, and applying chaotic heart rate variability (HRV) analysis to the prognosis of mortality in congestive heart failure (CHF). Distinguishing noise from chaos poses a major challenge in nonlinear dynamics theory since the addition of dynamic noise can make a non-chaotic nonlinear system exhibit stochastic chaos, a concept which is not well-defined and is the center of heated debate in chaos theory. A novel method for detecting dynamic noise in chaotic series is proposed in Part I of this thesis. In Part II, we show that linear and nonlinear analyses of HRV yield independent predictors of mortality. Specifically, sudden death is best predicted by frequency analysis whereas nonlinear and chaos indices are more selective for progressive pump failure death. These findings suggest a novel noninvasive probe for the clinical management of CHF patients. by Natalia M. Arzeno. M.Eng. 109 p. 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. Discriminating noise from chaos in heart rate variability : application to prognosis in heart failure Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f7477668-c106-497b-8fe0-36df1c0f64ca"> <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>Discriminating noise from chaos in heart rate variability : application to prognosis in heart failure</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2007</PublicationDate> <Authors> <Author> <DisplayName>Arzeno, Natalia M. (Natalia María Arzeno Soltero)</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>This thesis examines two challenging problems in chaos analysis: distinguishing deterministic chaos and stochastic (noise-induced) chaos, and applying chaotic heart rate variability (HRV) analysis to the prognosis of mortality in congestive heart failure (CHF). Distinguishing noise from chaos poses a major challenge in nonlinear dynamics theory since the addition of dynamic noise can make a non-chaotic nonlinear system exhibit stochastic chaos, a concept which is not well-defined and is the center of heated debate in chaos theory. A novel method for detecting dynamic noise in chaotic series is proposed in Part I of this thesis. In Part II, we show that linear and nonlinear analyses of HRV yield independent predictors of mortality. Specifically, sudden death is best predicted by frequency analysis whereas nonlinear and chaos indices are more selective for progressive pump failure death. These findings suggest a novel noninvasive probe for the clinical management of CHF patients.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>