Wavelet features for classification of vote snore sounds

Location and form of the upper airway obstruction is essential for a targeted therapy of obstructive sleep apnea (OSA). Utilizing snore sounds (SnS) to reveal the pathological characters of OSA patients has been the subject of scientific research for several decades. Fewer studies exist on the evaluation of SnS to identify the corresponding obstruction types in the upper airway. In this study, we propose a novel feature set based on wavelet transform with a support vector machine classifier to discriminate VOTE (velum, oropharyngeal lateral walls, tongue base and epiglottis) snore sounds labelled during drug-induced sleep endoscopy (DISE). Based on snore sound data collected from 24 snoring subjects, processed by a subject-independent 2-fold cross validation experiment, we can show that our wavelet features outperform the frequently-used acoustic features (formants, MFCC, power ratio, crest factor, fundamental frequency) at an WAR (weighted average recall) of 78.2 % and an UAR (unweighted average recall) of 71.2%, with an enhancement ranging from 5.1 % to 24.4% and 12.2% to 46.4% in WAR and UAR, respectively.