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HPSSを用いた呼吸音の自動分類
http://hdl.handle.net/10228/00009063
http://hdl.handle.net/10228/00009063734c87e3-2f76-400e-8267-0502f5fbb2c4
名前 / ファイル | ライセンス | アクション |
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LaSEINE-2021_009.pdf (2.9 MB)
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Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||||||||
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公開日 | 2023-02-01 | |||||||||||||||||
資源タイプ | ||||||||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||
資源タイプ | journal article | |||||||||||||||||
タイトル | ||||||||||||||||||
言語 | ja | |||||||||||||||||
タイトル | HPSSを用いた呼吸音の自動分類 | |||||||||||||||||
タイトル | ||||||||||||||||||
言語 | en | |||||||||||||||||
タイトル | Automatic Classification of Respiratory Sounds using HPSS | |||||||||||||||||
言語 | ||||||||||||||||||
言語 | jpn | |||||||||||||||||
著者 |
丸橋, 優生
× 丸橋, 優生× 浅谷, 尚希× 陸, 慧敏
WEKO
15968
× 神谷, 亨
WEKO
402
× 間普, 真吾× 木戸, 尚治 |
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抄録 | ||||||||||||||||||
内容記述タイプ | Abstract | |||||||||||||||||
内容記述 | 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. | |||||||||||||||||
言語 | en | |||||||||||||||||
書誌情報 |
ja : 医用画像情報学会雑誌 en : Medical Imaging and Information Sciences 巻 38, 号 2, p. 95-100, 発行日 2021-07-06 |
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言語 | ja | |||||||||||||||||
出版者 | 医用画像情報学会 | |||||||||||||||||
DOI | ||||||||||||||||||
関連タイプ | isIdenticalTo | |||||||||||||||||
識別子タイプ | DOI | |||||||||||||||||
関連識別子 | https://doi.org/10.11318/mii.38.95 | |||||||||||||||||
CRID | ||||||||||||||||||
関連タイプ | isIdenticalTo | |||||||||||||||||
識別子タイプ | URI | |||||||||||||||||
関連識別子 | https://cir.nii.ac.jp/crid/1390007128168714112 | |||||||||||||||||
日本十進分類法 | ||||||||||||||||||
主題Scheme | NDC | |||||||||||||||||
主題 | 492 | |||||||||||||||||
NCID | ||||||||||||||||||
収録物識別子タイプ | NCID | |||||||||||||||||
収録物識別子 | AN10156808 | |||||||||||||||||
ISSN | ||||||||||||||||||
収録物識別子タイプ | EISSN | |||||||||||||||||
収録物識別子 | 1880-4977 | |||||||||||||||||
ISSN | ||||||||||||||||||
収録物識別子タイプ | PISSN | |||||||||||||||||
収録物識別子 | 0910-1543 | |||||||||||||||||
著作権関連情報 | ||||||||||||||||||
権利情報 | Copyright (c) 2021 医用画像情報学会 | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | Respiratory Sounds | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | Computer Aided Diagnosis | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | Short-Time Fourier Transform | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | Harmonic/Percussive Sound Separation | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | Deep Learning | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | CNN | |||||||||||||||||
キーワード | ||||||||||||||||||
主題Scheme | Other | |||||||||||||||||
主題 | SVM | |||||||||||||||||
出版タイプ | ||||||||||||||||||
出版タイプ | VoR | |||||||||||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||
査読の有無 | ||||||||||||||||||
値 | yes | |||||||||||||||||
連携ID | ||||||||||||||||||
10838 |