1 research outputs found
Automatic classification of adventitious respiratory sounds: a (un)solved problem?
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory
sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we
studied the influence of event duration on automatic ARS classification, namely, how the creation of
the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set
of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze
vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four
classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional
neural networks) were evaluated on those tasks using an open access respiratory sound database.
(3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy
of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with
variable durations. (4) Conclusion: These results demonstrate the importance of experimental design
on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they
also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a
solved problem, as the algorithms’ performance decreases substantially under complex evaluation
scenarios.publishe