{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T18:40:11Z","timestamp":1710528011597},"reference-count":21,"publisher":"Acoustical Society of America (ASA)","issue":"6","content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2012,6,1]]},"abstract":"<jats:p>Used for centuries in the clinical practice, audible percussion is a method of eliciting sounds by tapping various areas of the human body either by finger tips or by a percussion hammer. Despite its advantages, pulmonary diagnostics by percussion is still highly subjective, depends on the physician\u2019s skills, and requires quiet surroundings. Automation of this well-established technique could help amplify its existing merits while removing the above drawbacks. In this work, clinical percussion signals from normal volunteers are decomposed into a sum of exponentially damped sinusoids (EDS) whose parameters are determined using the Matrix Pencil Method. Some EDS represent transient oscillation modes of the thorax\/abdomen excited by the percussion event, while others are associated with the noise. It is demonstrated that relatively few EDS are usually enough to accurately reconstruct the original signal. It is shown that combining the frequency and damping parameters of these most significant EDS allows for efficient classification of percussion signals into the two main types historically known as \u201cresonant\u201d and \u201ctympanic.\u201d This classification ability can provide a basis for the automated objective diagnostics of various pulmonary pathologies including pneumothorax. The algorithm can be implemented on an embedded platform for the battlefield and other emergency applications.<\/jats:p>","DOI":"10.1121\/1.4712018","type":"journal-article","created":{"date-parts":[[2012,6,14]],"date-time":"2012-06-14T23:02:13Z","timestamp":1339714933000},"page":"4690-4698","update-policy":"http:\/\/dx.doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":6,"title":["Estimating the parameters of audible clinical percussion signals by fitting exponentially damped harmonics"],"prefix":"10.1121","volume":"131","author":[{"given":"Moinuddin","family":"Bhuiyan","sequence":"first","affiliation":[{"name":"University of Windsor Electrical and Computer Engineering Department, , 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugene V.","family":"Malyarenko","sequence":"additional","affiliation":[{"name":"Tessonics Corp. , 2019 Hazel Street, Birmingham, Michigan 48009"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mircea A.","family":"Pantea","sequence":"additional","affiliation":[{"name":"University of Windsor Institute for Diagnostics Imaging Research, , 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman Gr.","family":"Maev","sequence":"additional","affiliation":[{"name":"University of Windsor Institute for Diagnostics Imaging Research, , 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfred 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":[[2012,6,14]]},"reference":[{"key":"2023071906242907900_c1","doi-asserted-by":"publisher","first-page":"1756","DOI":"10.1183\/09031936.95.08101756","article-title":"Chest percussion","volume":"8","year":"1995","journal-title":"Eur. 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