Pitch-jitter analysis of snoring sounds for the diagnosis of sleep apnea

Obstructive Sleep Apnea (OSA) is a disease in which airways involuntarily collapse during sleep, leading to serious consequences. About 10% of snorers suffer from OSA, unknown to them, nevertheless requiring medical attention. The current standard of diagnosis for OSA, polysomnography (PSG), requires that the patients spend one full day in a hospital, wired to a multitude of instruments. PSG is complicated, expensive, and unsuitable for mass screening of the population. OSA is commonly accompanied by snoring. Even though snoring carries vital information on the state of the airways, it has rarely been used in diagnosing OSA. In this paper, we present a mathematical model for snoring, and illustrate its usefulness in diagnosing OSA. We exploit similarities and differences between speech and snoring signals to separate the two, and, provide new features to diagnose OSA at low cost. Via experiments carried out in a hospital sleep-laboratory, we illustrate the importance of using noise reduction techniques to acquire snoring data with sufficient integrity.