Robust classification between a healthy subject and a patient with pulmonary emphysema using lung sound samples based on a stochastic approach
| Project/Area Number |
23500217
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Nagasaki University |
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Principal Investigator |
MATSUNAGA Shoichi 長崎大学, 工学(系)研究科(研究院), 教授 (90380815)
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| Co-Investigator(Kenkyū-buntansha) |
OGURI Kiyoshi 長崎大学, 大学院・工学研究科, 教授 (80325670)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2013: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 音認識 / モデル化 / 情報工学 / 機械学習 / 肺音 / 疾患者識別 / 異常音識別 / 統計的手法 / 副雑音 |
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| Research Abstract |
The objective of our study is to develop a home-use device to identify respiratory illness by detecting abnormal respiratory sounds with high accuracy. It can be assumed that the occurrence of noise in respiratory sounds is random, whereas adventitious sounds occur repeatedly in successive inspiratory/expiratory phases. Therefore, we proposed a classification method considering the occurrence tendency of adventitious sounds and noise in a series of respiratory sounds.
Furthermore, our proposed method took into account lung sound samples from multiple auscultation points in diagnosing a patient. After the calculation of the acoustic likelihood for each respiratory phase, patient diagnosis was carried out based on the comparison of the average likelihood of all auscultation points between a patient and a healthy subject. Our classification method significantly increased the classification performance.
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Report
(4 results)
Research Products
(22 results)
