Respiratory Artefact Removal in Forced Oscillation Measurements: A Machine Learning Approach.

Journal: IEEE transactions on bio-medical engineering
PMID: 28113281

Abstract

GOAL: Respiratory artefact removal for the forced oscillation technique can be treated as an anomaly detection problem. Manual removal is currently considered the gold standard, but this approach is laborious and subjective. Most existing automated techniques used simple statistics and/or rejected anomalous data points. Unfortunately, simple statistics are insensitive to numerous artefacts, leading to low reproducibility of results. Furthermore, rejecting anomalous data points causes an imbalance between the inspiratory and expiratory contributions.

Authors

  • Thuy T Pham
    Department of Electrical and Information Engineering, The University of Sydney, Sydney, N.S.W., Australia.
  • Cindy Thamrin
    Woolcock Institute of Medical Research.
  • Paul D Robinson
    Children's Hospital at Westmead.
  • Alistair L McEwan
    Department of Electrical and Information EngineeringThe University of Sydney.
  • Philip H W Leong
    Department of Electrical and Information EngineeringThe University of Sydney.

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