Automatic snoring signal analysis in sleep studies

Abstract

Snoring has been related to vibration of upper airway during sleep. It has been reported in the literature as a risk factor of different diseases, such as obstructive sleep apnea syndrome (OSAS) and other breathing abnormalities during sleep. Recently, our group has developed an automatic detector of snores to be applied in long-term sleep studies. This detector includes segmentation and classification blocs, based on a feedforward multilayer neural network. In this work, a complete procedure for detector validation is proposed, including annotation of different episodes: snores, sounds during inspiration and exhalation, speech and noise artifacts. A database of 948 episodes was manually annotated by a medical doctor in respiratory sound signals from 8 male subjects (4 normal snorers and 4 OSAS patients). The ratio non-snores/total annotated episodes was 53%. The detector shown a good performance, obtaining a sensitivity of 76,1%, a positive predictive value of 75,6% and a specificity of 82,8%. The automatic detector was applied to 6-hour snoring signals, corresponding to 37 subjects (12 females/25 males, 20 snorers/17 OSAS). Significant results shown differences between snorers and OSAS patients, and suggest that snore variability could be higher in OSAS patients.