Mixed-phase modeling in snore sound analysis.

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The gold standard of diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 channels of measurements requiring physical contact with sensors. PSG is expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in OSA diagnosis is not fully recognized yet. In this paper, we propose a novel model for SRS as the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis, and is capable of capturing acoustical changes brought about by the collapsing upper airways in OSA. We propose an algorithm based on higher-order-spectra (HOS) to jointly estimate the source and TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that TAR is indeed a mixed-phased signal and second-order statistics cannot fully characterize it. Night-time speech sounds can corrupt snore recordings and pose a challenge to snore based OSA diagnosis. We show that the TAR could be used to detect speech segments embedded in snores, and derive features to diagnose OSA via non-contact, low-cost instrumentation holding potential for a community screening device.