Classification between normal and abnormal respiratory sounds based on maximum likelihood approach

Shoichi Matsunaga, Katsuya Yamauchi, Masaru Yamashita, Sueharu Miyahara

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

43 被引用数 (Scopus)

抄録

In this paper, we have proposed a novel classification procedure for distinguishing between normal respiratory and abnormal respiratory sounds based on a maximum likelihood approach using hidden Markov models. We have assumed that each inspiratory/expiratory period consists of a time sequence of characteristic acoustic segments. The classification procedure detects the segment sequence with the highest likelihood and yields the classification result. We have proposed two elaborate acoustic modeling methods: one method is individual modeling for adventitious sound periods and for breath sound periods for the detection of abnormal respiratory sounds, and the other is a microphone-dependent modeling method for the detection of normal respiratory sounds. Classification experiments conducted using the former method revealed that this method demonstrated an increase of 19.1% in its recall rate of abnormal respiratory sounds as compared with the recall rate of a baseline method. It has also been revealed that the latter modeling method demonstrates an increase in its recall rate for the detection of not only normal respiratory sounds but also for abnormal respiratory sounds. These experimental results have confirmed the validity of our proposed classification procedure.

本文言語英語
ホスト出版物のタイトル2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
ページ517-520
ページ数4
出版ステータス出版済み - 10 1 2009
外部発表はい
イベント2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, 台湾省、中華民国
継続期間: 4 19 20094 24 2009

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

その他

その他2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
国/地域台湾省、中華民国
CityTaipei
Period4/19/094/24/09

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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