Variability analysis of the respiratory volume based on non-linear prediction methods

  • P. Caminal
  • L. Dominge
  • B. F. Giraldo
  • M. Vallverdu
  • S. Benito
  • G. Vázquez
  • D. Kaplan
Article

Abstract

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.

Keywords

Respiratory pattern variability Non-linear prediction methods Pressure support ventilation 

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Copyright information

© IFMBE 2004

Authors and Affiliations

  • P. Caminal
    • 1
  • L. Dominge
    • 1
  • B. F. Giraldo
    • 1
  • M. Vallverdu
    • 1
  • S. Benito
    • 2
  • G. Vázquez
    • 2
  • D. Kaplan
    • 3
  1. 1.Biornedical Engineering Research Centre, Departament ESAIITechnical University of CataloniaSpain
  2. 2.Department of Intensive Care MedicineHospital de la Santa Creu i Sant PauBarcelonaSpain
  3. 3.Department of Mathematics & Computer ScienceMacalester CollegeMinnesotaUSA

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