Capnographic analysis for disease classification
Name
843769131-MIT.pdf
Description
Full printable version
Size
8.18 MB
Format
Adobe PDF
Checksum (MD5)
ba0207000ae1710ffa64e0baa69e395b
Author(s)
Asher, Rebecca J. (Rebecca Jennie)
Advisor(s)
George C. Verghese.
Date Issued
2012
Publisher
Massachusetts Institute of Technology
Abstract
Existing methods for extracting diagnostic information from carbon dioxide in the exhaled breath are qualitative, through visual inspection, and therefore imprecise. In this thesis, we quantify the CO₂ waveform, or capnogram, in order to discriminate among various lung disorders. Quantitative analyses of the capnogram are conducted by extracting several physiological waveform features and performing classification by discriminant analysis with voting. Our classification methods are tested in distinguishing between records from subjects with normal lung function and patients with cardiorespiratory disease. In a second step, we discriminate between capnograms from patients with obstructive lung disease (chronic obstructive pulmonary disease) and those with restrictive lung disease (congestive heart failure). Our results demonstrate the diagnostic potential of capnography.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 73-76).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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