Can we discriminate between apnea and hypopnea using audio signals?

Obstructive sleep apnea (OSA) affects up to 14% of the population. OSA is characterized by recurrent apneas and hypopneas during sleep. The apnea-hypopnea index (AHI) is frequently used as a measure of OSA severity. In the current study, we explored the acoustic characteristics of hypopnea in order to distinguish it from apnea. We hypothesize that we can find audio-based features that can discriminate between apnea, hypopnea and normal breathing events. Whole night audio recordings were performed using a non-contact microphone on 44 subjects, simultaneously with the polysomnography study (PSG). Recordings were segmented into 2015 apnea, hypopnea, and normal breath events and were divided to design and validation groups. A classification system was built using a 3-class cubic-kernelled support vector machine (SVM) classifier. Its input is a 36-dimensional audio-based feature vector that was extracted from each event. Three-class accuracy rate using the hold-out method was 84.7%. A two-class model to separate apneic events (apneas and hypopneas) from normal breath exhibited accuracy rate of 94.7%. Here we show that it is possible to detect apneas or hypopneas from whole night audio signals. This might provide more insight about a patient's level of upper airway obstruction during sleep. This approach may be used for OSA severity screening and AHI estimation.

I. Introduction

Sleep-disordered breathing (SDB) is a group of common disorders that affect up to 20% of the population [1]. Its prevalence has substantially increased over the past two decades: an increase of more than 14% in the North-American adult population [2]. The most prominent disorder among this group is obstructive sleep apnea (OSA), which is characterized by recurrent events of partial or complete collapse of the upper airway during sleep (i.e., hypopnea and apnea). OSA can lead to excessive daytime sleepiness, cardiovascular morbidity, and death [3]. Severity of OSA is measured by the apnea-hypopnea index (ARI), which is the average number of apnea and hypopnea events per hour of sleep.