Neural network analysis of the volumetric capnogram to detect pulmonary embolism

Manish M. Patel, Daniel B. Rayburn, Jane A. Browning, Jeffrey A. Kline

Research output: Contribution to journalArticle

12 Scopus citations


Background: Pulmonary embolism (PE) produces ventilation/perfusion mismatch that may be manifested in various variables of the volume-based capnogram (VBC). We hypothesized that a neural network (NN) system could detect changes in VBC variables that reflect the presence of a PE. Methods: A commercial VBC system was used to record multiple respiratory variables from consecutive expiratory breaths. Data from 12 subjects (n = 6 PE+ and n = 6 PE-) were used as input to a fully connected back-propagating NN for model development. The derived model was tested in a prospective, observational study at an urban teaching hospital. Volumetric capnograms were then collected on 53 test subjects: 30 subjects with PE confirmed by pulmonary angiography or diagnostic scintillation lung scan; and 23 subjects without PE based on pulmonary angiography. The derived NN model was applied to VBC data from the test population. Results: Seventeen VBC variables were used by the derived NN model to generate a numeric probability of PE. When the derived NN model was applied to VBC data from the 53 test subjects, PE was detected with a sensitivity of 100% (95% CI = 89% to 100%) and a specificity of 48% (95% CI = 27% to 69%). The likelihood ratio positive [LR(+)] for the VBC-NN test was 1.82 and the LR(-) was 0.1. Conclusion: This study demonstrates the feasibility of developing a rapid, noninvasive breath test for diagnosing PE using volumetric capnography and NN analysis.

Original languageEnglish (US)
Pages (from-to)1325-1332
Number of pages8
Issue number5
StatePublished - Jan 1 1999
Externally publishedYes


  • Artificial intelligence
  • Capnography
  • Diagnosis
  • Pulmonary embolism
  • Respiratory monitoring
  • Thromboembolism

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine
  • Cardiology and Cardiovascular Medicine

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