Novel Features for Automated Lung Function Diagnosis in Spontaneously Breathing Infants

  • Authors:

  • Steffen Leonhardt;Vojislav Kecman

  • Affiliations:

  • Philips Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Germany;The University of Auckland, Auckland, New Zealand

  • Venue:
  • AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

A comparative analysis of 14 classic and 23 novel mathematical features for diagnosing lung function in infants is presented. The data set comprises tidal breathing flow volume loops of 195 spontaneously breathing infants aged 3 to 24 months, with 9 known breathing problems (diseases). The data set is sparse. Diagnostic power was evaluated using support vector machines featuring both polynomial and Gaussian kernels in a rigorous experimental setting (100 runs for random splits of data into the training set (90% of data) and test set (10% of data)). Novel features achieve lower error rates than the old ones.