Automatic Multi-Level In-Exhale Segmentation and Enhanced Generalized S-Transform for wheezing detection.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 26, 2019

Abstract

BACKGROUND AND OBJECTIVE: Wheezing is a common symptom of patients caused by asthma and chronic obstructive pulmonary diseases. Wheezing detection identifies wheezing lung sounds and helps physicians in diagnosis, monitoring, and treatment of pulmonary diseases. Different from the traditional way to detect wheezing sounds using digital image process methods, automatic wheezing detection uses computerized tools or algorithms to objectively and accurately assess and evaluate lung sounds. We propose an innovative machine learning-based approach for wheezing detection. The phases of the respiratory sounds are separated automatically and the wheezing features are extracted accordingly to improve the classification accuracy.

Authors

  • Hai Chen
    Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau; School of Information Technology, Beijing Normal University, Zhuhai, Zhuhai, China. Electronic address: isabell@bnuz.edu.cn.
  • Xiaochen Yuan
    Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau. Electronic address: xcyuan@must.edu.mo.
  • Jianqing Li
    School of Instrument Science and Engineering, Southeast University, Sipailou 2, Nanjing 210096, China.
  • Zhiyuan Pei
    School of Information Technology, Beijing Normal University, Zhuhai, Zhuhai, China. Electronic address: 1501040082@mail.bnuz.edu.cn.
  • Xiaobin Zheng
    Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Electronic address: zhxbin@sysu.edu.cn.

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