Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification.

Journal: PloS one
Published Date: Sep 30, 2015

Abstract

BACKGROUND: Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases.

Authors

  • Jinxiang Xi
    School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
  • Weizhong Zhao
    College of Information Engineering, Xiangtan University, Xiangtan, Hunan Province, China; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Jefferson, Arkansas, United States of America.
  • Jiayao Eddie Yuan
    School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
  • JongWon Kim
    College of Engineering, University of Georgia, Athens, Georgia, United States of America.
  • Xiuhua Si
    Department of Mechanical Engineering, California Baptist University, Riverside, California, United States of America.
  • Xiaowei Xu
    Department of Information Science, University of Arkansas, Little Rock, Arkansas, United States of America.

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