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George Verghese and Thomas Heldt. Haslam, Bryan (Bryan Todd) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2012-01-12T19:32:45Z 2012-01-12T19:32:45Z 2011 2011 http://hdl.handle.net/1721.1/68501 770669353 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 161-167). In this thesis I examine several ways of extracting information from wearable monitors so as to help make clinical decisions. Wearable physiological sensors are developing rapidly, and pose a possible part of the solution to the demands of an aging population and rising health care costs. It is important that the data produced by such sensors be processed into information that is clinically relevant and will have an impact on the practice of medicine. I collected data in an ambulatory setting from several wearable physiological sensors, including electrocardiogram, arterial blood pressure, pulse plethysmograph, respiration and acceleration. Using this data set, I demonstrated a few approaches - including signal processing, and algorithms based on the application of physiological models - to extract clinically relevant information. These approaches are potentially of interest to both device makers interested in developing wearable monitors, and to clinicians who will be using such monitors in the future. by Bryan Haslam. S.M. 167 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. Extracting clinically-actionable information from wearable physiological monitors Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="87e9765c-5d29-47c9-b4d7-2bf1928f1e17"> <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>Extracting clinically-actionable information from wearable physiological monitors</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2011</PublicationDate> <Authors> <Author> <DisplayName>Haslam, Bryan (Bryan Todd)</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>In this thesis I examine several ways of extracting information from wearable monitors so as to help make clinical decisions. Wearable physiological sensors are developing rapidly, and pose a possible part of the solution to the demands of an aging population and rising health care costs. It is important that the data produced by such sensors be processed into information that is clinically relevant and will have an impact on the practice of medicine. I collected data in an ambulatory setting from several wearable physiological sensors, including electrocardiogram, arterial blood pressure, pulse plethysmograph, respiration and acceleration. Using this data set, I demonstrated a few approaches - including signal processing, and algorithms based on the application of physiological models - to extract clinically relevant information. These approaches are potentially of interest to both device makers interested in developing wearable monitors, and to clinicians who will be using such monitors in the future.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>