Subject independent identification of breath sounds components using multiple classifiers

Breath sounds have been shown very valuable for diagnosis of obstructive sleep apnea. In this study, we present a subject independent method for automatic classification of breath and related sounds during sleep. An experienced operator manually labelled segments of breath sounds from 11 sleeping subjects as: inspiration, expiration, inspiratory snoring, expiratory snoring, wheezing, other noise, and non-audible. Ten features were extracted and fed into 3 different classifiers: näıve Bayes, Support Vector Machine, and Random Forest. Leave-one-out method was used in which data from each subject, in turn, is evaluated using models trained with all other subject. Mean accuracy for concurrent classification of all 7 classes reached 85.4%. Mean accuracy for separating data into 2 classes, snoring and non-snoring, reached 97.8%. To our knowledge, these are the highest accuracies achieved in automatic classification of all breath sounds components concurrently and for snoring, in a subject independent model.