{"id":{"repo_id":"wvu","oai_identifier":"oai:researchrepository.wvu.edu:etd-2487"},"canonical_url":"https://search.dev.ndltd.org/etd/wvu/oai:researchrepository.wvu.edu:etd-2487","repository":{"repo_id":"wvu","name":"West Virginia University","base_url":"https://researchrepository.wvu.edu/do/oai/"},"display":{"title":"Acoustical and flow characteristics of a cough as an index of pulmonary function in the guinea pig","abstract":"Human studies indicate that cough sound and flow analysis may be useful for diagnosing pulmonary abnormalities. The purpose of this study was to evaluate an animal model for cough sound and flow analysis. A system was designed to expose guinea pigs to aerosols of citric acid (0.39M) and record resulting coughs at different stages of chemically induced specific airway resistance (sRAW). Coughs were divided into three categories (low sRAW, n = 113; moderate sRAW, n = 143; high sR AW, n = 93). 124 cough sound parameters were derived from the analysis of the sound pressure waves recorded during the cough. A principal component analysis was performed on the acquired data, and the resulting parameters were used to train a single neuron feed-forward back propagation neural network. The classification system was able to correctly discriminate between members of the high and low airway constriction groups with an accuracy of 0.946 and a sensitivity and specificity of 0.893.","abstract_html":"Human studies indicate that cough sound and flow analysis may be useful for diagnosing pulmonary abnormalities. The purpose of this study was to evaluate an animal model for cough sound and flow analysis. A system was designed to expose guinea pigs to aerosols of citric acid (0.39M) and record resulting coughs at different stages of chemically induced specific airway resistance (sRAW). Coughs were divided into three categories (low sRAW, n = 113; moderate sRAW, n = 143; high sR AW, n = 93). 124 cough sound parameters were derived from the analysis of the sound pressure waves recorded during the cough. A principal component analysis was performed on the acquired data, and the resulting parameters were used to train a single neuron feed-forward back propagation neural network. The classification system was able to correctly discriminate between members of the high and low airway constriction groups with an accuracy of 0.946 and a sensitivity and specificity of 0.893.","abstract_has_math":false,"creators":["Day, Joshua W."],"institution":null,"degree_name":"MS","degree_level":"Thesis","degree_discipline":"Lane Department of Computer Science and Electrical Engineering","degree_department":null,"school":null,"contributors":["Mark Jerabek","Dave Frazer."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004-08-01T07:00:00Z","date_published":"2004-08-01T07:00:00Z","updated_at":"2026-07-24T06:15:47Z","subjects":["Electrical engineering","Biomedical engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://researchrepository.wvu.edu/etd/1484"],"render_values":[{"text":"https://researchrepository.wvu.edu/etd/1484","href":"https://researchrepository.wvu.edu/etd/1484","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.33915/etd.1484","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mark Jerabek","Dave Frazer."]},{"key":"dc:creator","label":"Author","values":["Day, Joshua W."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-01-17T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Lane Department of Computer Science and Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical engineering","Biomedical engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.33915/etd.1484","https://researchrepository.wvu.edu/etd/1484"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Human studies indicate that cough sound and flow analysis may be useful for diagnosing pulmonary abnormalities. The purpose of this study was to evaluate an animal model for cough sound and flow analysis. A system was designed to expose guinea pigs to aerosols of citric acid (0.39M) and record resulting coughs at different stages of chemically induced specific airway resistance (sRAW). Coughs were divided into three categories (low sRAW, n = 113; moderate sRAW, n = 143; high sR AW, n = 93). 124 cough sound parameters were derived from the analysis of the sound pressure waves recorded during the cough. A principal component analysis was performed on the acquired data, and the resulting parameters were used to train a single neuron feed-forward back propagation neural network. The classification system was able to correctly discriminate between members of the high and low airway constriction groups with an accuracy of 0.946 and a sensitivity and specificity of 0.893."]},{"key":"dc:title","label":"Title","values":["Acoustical and flow characteristics of a cough as an index of pulmonary function in the guinea pig"]}]}],"canonical_facts":{"dc:contributor":["Mark Jerabek","Dave Frazer."],"dc:creator":["Day, Joshua W."],"dc:date.available":["2019-01-17T08:00:00Z"],"dc:description.abstract":["Human studies indicate that cough sound and flow analysis may be useful for diagnosing pulmonary abnormalities. The purpose of this study was to evaluate an animal model for cough sound and flow analysis. A system was designed to expose guinea pigs to aerosols of citric acid (0.39M) and record resulting coughs at different stages of chemically induced specific airway resistance (sRAW). Coughs were divided into three categories (low sRAW, n = 113; moderate sRAW, n = 143; high sR AW, n = 93). 124 cough sound parameters were derived from the analysis of the sound pressure waves recorded during the cough. A principal component analysis was performed on the acquired data, and the resulting parameters were used to train a single neuron feed-forward back propagation neural network. The classification system was able to correctly discriminate between members of the high and low airway constriction groups with an accuracy of 0.946 and a sensitivity and specificity of 0.893."],"dc:identifier":["https://doi.org/10.33915/etd.1484","https://researchrepository.wvu.edu/etd/1484"],"dc:subject":["Electrical engineering","Biomedical engineering"],"dc:title":["Acoustical and flow characteristics of a cough as an index of pulmonary function in the guinea pig"],"thesis:degree_discipline":["Lane Department of Computer Science and Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T06:15:47Z"}