ABSTRACT
Obstructive Sleep Apnea (OSA) patients have frequent breathing obstructions and upper airway (UA) collapse during sleep. It is clinically important to estimate OSA severity separately for Rapid Eye Movement (REM) and non-REM (NREM) sleep states, but the task requires Polysomnography (PSG) which uses about 15-20 body contact sensors and subjective assessment. Almost all OSA patients snore. Vibration in narrowed UA muscles cause snoring in OSA. Moreover, as sleep states are associated with distinct breathing patterns and UA muscle tone, REM/NREM specific information must be available via snore/breathing sounds. Our previous works have shown that snoring carries significant information related to REM/NREM sleep states and OSA. We hypothesized that such information from snoring sound could be used to characterize OSA specific to REM/NREM sleep states independent of PSG. We acquired overnight audio recording from 91 patients (56 males and 35 females) undergoing PSG and labeled snore sounds as belonging to REM/NREM stages based on PSG. We then developed features to capture REM/NREM specific information and trained logistic regression (LR) classifier models to map snore features to OSA severity bands. Considering separate LR models for males and females, we achieved 94-100% sensitivity (84-89% specificity) for NREM stages at the OSA severity threshold of 30 events/h. Corresponding sensitivity for REM stages were 92-97% with specificity 83-85%. Results indicate that it is feasible to estimate severe/non-severe OSA in REM/NREM sleep based on snore/breathing sounds alone, acquired using simple bedside sound acquisition devices such as mobile phones.
