title: Robust features for detection of crackles: an exploratory study.
abstract: Crackles are adventitious and explosive respiratory sounds that can be classified as fine or coarse. These sounds are usually associated with cardiopulmonary diseases such as the chronic obstructive pulmonary disease. In this work seven different features were tested with the objective to identify the best subset of features that allows a robust detection of coarse crackles. Some of the features used in this study are new, namely those based on the local entropy, on the Teager energy and on the residual fit of a Generalized Autoregressive Conditional Heteroskedasticity process. The best features as a function of the number of features used in classification were identified having into account the Matthews correlation coefficient. The best individual feature was based on the local entropy. A significant improvement in the performance was obtained by using the feature based on local entropy and the feature based on the wavelet packed stationary transform - no stationary transform. The addition of more features only allows a smaller improvement.
authors.forename: L
authors.initials: L
authors.lastname: Mendes
authors.forename: P
authors.initials: P
authors.lastname: Carvalho
authors.forename: C A
authors.initials: CA
authors.lastname: Teixeira
authors.forename: R P
authors.initials: RP
authors.lastname: Paiva
authors.forename: J
authors.initials: J
authors.lastname: Henriques
mesh_terms: D000465:Algorithms; D019277:Entropy; D017709:Fractals; D006801:Humans; D010363:Pattern Recognition, Automated; D012135:Respiratory Sounds / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted