Mixed-phase modeling of snore sounds within a nonlinear framework for component identification

Summary form only given. Snoring is the earliest and the most prevalent symptom of obstructive sleep apnea (OSA), a serious disease caused by the collapse of upper airways during sleep. In this paper, we model snore related sounds (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. To estimate components of the TAR/source model, preserving true phase information, we develop a novel non-linear framework based on higher-order statistics (HOS). Working on a clinical database of signals, we show that TAR is indeed a mixed-phased signal, and thus correlation (power spectrum) based conventional techniques cannot completely describe snoring sounds.