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논문 기본 정보

자료유형
학술대회자료
저자정보
Koki Minami (Kyushu Institute of Technology) Huimin Lu (Kyushu Institute of Technology) Hyoungseop Kim (Kyushu Institute of Technology) Shingo Mabu (Yamaguchi University) Yasushi Hirano (Yamaguchi University) Shoji Kido (Osaka University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2019
발행연도
2019.10
수록면
804 - 807 (4page)

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초록· 키워드

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Auscultation of respiratory sounds is very important for discovering the respiratory disease. However, there is no quantitative evaluation method for the diagnosis of respiratory sounds until now. It is necessary to develop a system to support the diagnosis of respiratory sounds. In addition, there are few studies using dataset suitable for generating realistic classification models that can be used in clinical sites in algorithm development for automatic analysis of respiratory sounds. We describe the development of an algorithm for the automatic classification of the large-scale respiratory sound dataset used in ICBHI 2017 Challenge as containing crackles, containing wheeze, containing both, and normal. Our approach consists of two major components. Firstly, transformation of one-dimensional signals into two-dimensional time-frequency representation images using short-time Fourier transform and continuous wavelet transform. Secondly, classification of transferred images using convolutional neural networks. In this paper, we apply our proposed method to 920 respiratory sound data, and achieve score of 28[%], harmonic score of 81[%], sensitivity of 54[%] and specificity of 42[%].

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Abstract
1. INTRODUCTION
2. METHOD
3. EXPERIMENT
4. DISCUSSION
5. CONCLUSION
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