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dc.contributor.advisorJohn V. Guttag.en_US
dc.contributor.authorSyed, Zeeshan Hassan, 1980-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2005-06-02T19:38:33Z
dc.date.available2005-06-02T19:38:33Z
dc.date.copyright2003en_US
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/18018
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.en_US
dc.descriptionIncludes bibliographical references (p. 123-127).en_US
dc.description.abstractAt every annual exam, the primary care physician uses a stethoscope to listen for cardiac abnormalities. This approach is non-invasive, inexpensive, and fast. It is also highly unreliable. Over 80% of the people referred to cardiologists as suffering from the most commonly diagnosed condition, mitral valve prolapse (MVP), do not have this condition. Working in conjunction with cardiologists at MGH, we developed a robust, low cost, easy to use tool that can be employed to diagnose MVP in the office of primary care physicians. The system fuses signals from an electronic stethoscope and a two-lead EKG, and uses software running on a desktop or laptop computer to make a diagnosis. We also provide a number of novel audiovisual diagnostic aids. These allow physicians to visualize both individual heart beats and a visual-prototypical heart beat constructed from a sequence of beats. They also permit doctors to listen to an audio-prototypical heart-beat, audio enhanced heart-beats that amplify clinically significant sounds, and slowed down heart-beats that make it easier to separate clinically relevant cardiac events. We tested our system on 51 patients. The number of false positives was reduced to approximately 10%. While there is no generally accepted statistic on false negatives, anecdotal experience indicates that our system also outperforms physicians in this respect.en_US
dc.description.statementofresponsibilityby Zeeshan Hassan Syed.en_US
dc.format.extent127 p.en_US
dc.format.extent7383965 bytes
dc.format.extent7400200 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleMIT Automated Auscultation Systemen_US
dc.title.alternativeMassachusetts Institute of Technology Automated Auscultation Systemen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc57206436en_US


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