Analysis of Tidal Breathing Flow Volume Loops for Automated Lung-Function Diagnosis in Infants

Abstract

Lung-function analysis in the age group below 5 years has not yet found its way into clinical routine. One possible candidate for routine lung testing in this age group is the analysis of tidal breathing flow-volume (TBFV) loops, a technique that has not yet proven to be capable of detecting obstructive and other lung disorders at an early stage. We present a new set of mathematical features useful to analyze TBFV loops. These new features attempt to describe more complex properties of the loops, thus imitating medical judgment of the curves (e.g., "round," "triangular," etc.) in a "linguistic" manner. Furthermore, we introduce support vector machines (SVMs) as a method for automated classification of diseases. In a retrospective clinical trial on 195 spontaneously breathing infants aged 3 to 24 months, the discriminant power of individual features and the overall diagnostic performance of SVMs is investigated and compared with the results obtained with traditional Bayes' classifiers. We demonstrate that the proposed new features perform better in all examined disease groups and that depending on the disease, the classification error can be reduced by up to 50%. We conclude that TBFV loops may have a much stronger discriminant power than previously thought.