{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T03:20:21Z","timestamp":1771903221641,"version":"3.50.1"},"reference-count":26,"publisher":"Acoustical Society of America (ASA)","issue":"1","content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2012,1,1]]},"abstract":"<jats:p>Chest percussion is a traditional technique used for the physical examination of pulmonary injuries and diseases. It is a method of tapping body parts with fingers or small instruments to evaluate the size, consistency, borders, and presence of fluid\/air in the lungs and abdomen. Percussion has been successfully used for the diagnosis of such potentially lethal conditions as traumatic and tension pneumothorax. This technique, however, has certain shortcomings, including limitations of the human ear and the subjectivity of the administrator, that lead to overall low sensitivity. Automation of the method by using a standardized percussion source and computerized classification of digitized signals would remove the subjective factor and other limitations of the technique. It would also enable rapid on-site diagnostics of pulmonary traumas when thorough clinical examination is impossible. This paper lays the groundwork for an objective signal classification approach based on a general physical model of a damped harmonic oscillator. Using this concept, critical parameters that effectively subdivide percussion signals into three main groups, historically known as \u201ctympanic,\u201d \u201cresonant,\u201d and \u201cdull,\u201d are identified, opening the possibility for automated diagnostics of air\/liquid inclusions in the thorax and abdomen. The key role of damping in forming the character of the percussion signal is investigated using a 3D thorax phantom. The contribution of the abdominal component into the complex multimode spectrum of chest percussion signals is demonstrated.<\/jats:p>","DOI":"10.1121\/1.3665985","type":"journal-article","created":{"date-parts":[[2012,1,17]],"date-time":"2012-01-17T00:59:26Z","timestamp":1326761966000},"page":"608-619","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":12,"title":["A physical approach to the automated classification of clinical percussion sounds"],"prefix":"10.1121","volume":"131","author":[{"given":"M. A.","family":"Pantea","sequence":"first","affiliation":[{"name":"Institute for Diagnostics Imaging Research, University of Windsor , 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. Gr.","family":"Maev","sequence":"additional","affiliation":[{"name":"Institute for Diagnostics Imaging Research, University of Windsor , 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. V.","family":"Malyarenko","sequence":"additional","affiliation":[{"name":"Tessonics Corporation, 2019 Hazel Street, Birmingham, Michigan 48009"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. E.","family":"Baylor","sequence":"additional","affiliation":[{"name":"Detroit Medical Center, 4201 St. Antoine Street, Detroit, Michigan 48201"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"231","published-online":{"date-parts":[[2012,1,13]]},"reference":[{"key":"2023072704412176900_c1","doi-asserted-by":"publisher","first-page":"021010","DOI":"10.1115\/1.2827358","article-title":"Experimental and computational models for simulating sound propagation within the lungs","volume":"130","year":"2008","journal-title":"J. Vib. 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