Influence of Event Duration on Automatic Wheeze Classification

TL;DR

This study investigates how the duration of respiratory events influences the accuracy of automatic wheeze classification, highlighting the impact of class design on classifier performance.

Contribution

It demonstrates the significant effect of non-wheeze event duration on classifier accuracy in respiratory sound analysis.

Findings

High accuracy (98% sensitivity, 95% specificity) on initial classifier.

Performance drops to 55% sensitivity and 76% specificity when event duration is altered.

Experimental design critically affects wheeze classification evaluation.

Abstract

Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifiers on an open access respiratory sound database, with the best one reaching sensitivity and specificity values of 98% and 95%, respectively. Then, by changing one parameter in the design of the non-wheeze class, i.e., event duration, the best classifier only reached sensitivity and specificity values of 55% and 76%, respectively. These results demonstrate the importance of experimental design on the assessment of wheeze classification algorithms' performance.