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Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration


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Title: Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration
Authors: Yamashita, Masaru / Matsunaga, Shoichi / Miyahara, Sueharu
Issue Date: May-2011
Publisher: IEEE
Citation: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.693-696
Abstract: In this paper, we propose a robust classification strategy for distinguishing between a healthy subject and a patient with pulmonary emphysema on the basis of lung sounds. A symptom of pulmonary emphysema is that almost all lung sounds include some abnormal (i.e., adventitious) sounds. However, the great variety of possible adventitious sounds and noises at auscultation makes high-accuracy detection difficult. To overcome this difficulty, our strategy is to adopt a two-step classification approach based on the detection of "confident abnormal respiration." In the first step, hidden Markov models and bigram models are used for acoustic features and the occurrence of acoustic segments in each abnormal respiratory period, respectively, to calculate two kinds of stochastic likelihoods: the highest likelihood for a segment sequence to be abnormal respiration and the likelihood for normal respiration. In the second step, the patients are identified on the basis of the detection of confident abnormal respiration, which is when difference between these two likelihoods is larger than a predefined threshold. Our strategy achieved the highest classification rate of 88.7% between healthy subjects and patients among three basic classification strategies, which shows the validity of our approach.
Description: ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) : Prague, Czech Republic, 2011.05.22-2011.05.27
Keywords: acoustic model / adventitious sound / lung sound
URI: http://hdl.handle.net/10069/26236
ISBN: 978-1-4577-0538-0 / 978-1-4577-0537-3
ISSN: 15206149
DOI: 10.1109/ICASSP.2011.5946498
Rights: © 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Type: Conference Paper
Text Version: author
Appears in Collections:Conference Paper

Citable URI : http://hdl.handle.net/10069/26236

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