{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/79320"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/79320","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Capnographic analysis for disease classification","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Asher, Rebecca J. (Rebecca Jennie)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["George C. Verghese."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:21:02Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/79320","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["George C. Verghese."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Asher, Rebecca J. (Rebecca Jennie)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-06-17T19:56:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2013-06-17T19:56:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/79320"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Capnographic analysis for disease classification"]}]}],"canonical_facts":{"dc:contributor.advisor":["George C. Verghese."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Asher, Rebecca J. (Rebecca Jennie)"],"dc:date.accessioned":["2013-06-17T19:56:02Z"],"dc:date.available":["2013-06-17T19:56:02Z"],"dc:date.issued":["2012"],"dc: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)."],"dc:description.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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/79320"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Capnographic analysis for disease classification"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:02Z"}