-
- MARUBASHI Yuki
- Kyushu Institute of Technology
-
- ASATANI Naoki
- Kyushu Institute of Technology
-
- LU Huimin
- Kyushu Institute of Technology
-
- KAMIYA Tohru
- Kyushu Institute of Technology
-
- MABU Shingo
- Yamaguchi University
-
- KIDO Shoji
- Osaka University
Bibliographic Information
- Other Title
-
- HPSS を用いた呼吸音の自動分類
Search this article
Abstract
<p>Respiratory disease is a serious illness that accounts for three of the top ten causes of death in the world, and approximately eight million people died worldwide each year. Early detection and early treatment are important for the prevention of illness due to these diseases. Currently, auscultation is performed for the diagnosis of respiratory diseases,however there is a problem that quantitative diagnosis is difficult. Therefore, in this paper, we propose a new automatic classification method of respiratory sounds to support the diagnosis of respiratory diseases on auscultation. In the proposed method, respiratory sound data is converted into a spectrogram image by applying the short-time Fourier transform. Then,we apply HPSS (Harmonic/Percussive Sound Separation) algorithm to the respiratory sound spectrogram to separate it into a harmonic spectrogram and a percussive spectrogram. The three generated spectrograms are used for classification of respiratory sounds by CNN (Convolutional Neural Network) and SVM (Support Vector Machine) classifiers. Our proposed method obtained superior classification performance compared to the case without applying HPSS and satisfactory results are obtained.</p>
Journal
-
- Medical Imaging and Information Sciences
-
Medical Imaging and Information Sciences 38 (2), 95-100, 2021-07-06
MEDICAL IMAGING AND INFORMATION SCIENCES
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390007128168714112
-
- NII Article ID
- 130008062643
-
- NII Book ID
- AN10156808
-
- ISSN
- 18804977
- 09101543
-
- HANDLE
- 10228/00009063
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- IRDB
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed