Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

  • Qing Zhang
  • , Jing Zhang
  • , Jiajun Yuan
  • , Huajie Huang
  • , Yuhang Zhang
  • , Baoqin Zhang
  • , Gaomei Lv
  • , Shuzhu Lin
  • , Na Wang
  • , Xin Liu
  • , Mingyu Tang
  • , Yahua Wang
  • , Hui Ma
  • , Lu Liu
  • , Shuhua Yuan
  • , Hongyuan Zhou
  • , Jian Zhao
  • , Yongfu Li*
  • , Yong Yin*
  • , Liebin Zhao*
  • Guoxing Wang, Yong Lian
*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

It is important to continuously monitor our respiratory system to prevent us from suffering respiratory-releated diseases. This demands for an automatic respiratory sounds software to speed up diagnosis and to reduce the workload of physicians. In the IEEE BioCAS 2022 conference, we have organized the first grand challenge on respiratory sound classification using the paediatric respiratory sound (SPRSound). This event has invited 45 teams with more than 100 open source entries and the top 5 teams are invited to present their works in the IEEE BioCAS 2022.

Original languageEnglish
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationIntelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages213-217
Number of pages5
ISBN (Electronic)9781665469173
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan, Province of China
Duration: 13 Oct 202215 Oct 2022

Publication series

NameBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

Conference

Conference2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/TerritoryTaiwan, Province of China
CityTaipei
Period13/10/2215/10/22

Keywords

  • Open-source
  • and machine-learning models
  • data augmentation
  • deep-learning models
  • feature extractions
  • grand challenge
  • respiratory sound classification

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