An open auscultation dataset for machine learning-based respiratory diagnosis studies.

Journal: JASA express letters
Published Date: May 1, 2024

Abstract

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.

Authors

  • Guanyu Zhou
    Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China. Electronic address: zhou.guanyu@outlook.com.
  • Chengjian Liu
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China.
  • Xiaoguang Li
    Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, China.
  • Sicong Liang
    College of Engineering, Peking University, Beijing, 100871, Chinazgylqhbdbyl@sina.com, pku_lcj@pku.edu.cn, lixiaoguangpuh3@bjmu.edu.cn, liangsicong@pku.edu.cn, wangruichen@pku.edu.cn, huangxun@pku.edu.cn.
  • Ruichen Wang
    College of Engineering, Peking University, Beijing, 100871, Chinazgylqhbdbyl@sina.com, pku_lcj@pku.edu.cn, lixiaoguangpuh3@bjmu.edu.cn, liangsicong@pku.edu.cn, wangruichen@pku.edu.cn, huangxun@pku.edu.cn.
  • Xun Huang
    Department of Bone and Joint Surgery, The First Affiliated Hospital of Jinan University.

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