Higher-order spectra for the estimation of total-airway-response (TAR) in snore-based diagnosis of apnoea

Obstructive sleep apnoea (OSA) is a serious disease caused by the collapse of upper airways during sleep. Untreated OSA is a public health concern. However, over 90% patients remain undiagnosed at present due to the unavailability of a convenient diagnostic tool. Snoring is the earliest and the most prevalent symptom of OSA. In this paper, we model snore related sounds as the response of a non-minimum phase filter (total airways response, TAR) to a source excitation at the input. Based on higher-order statistics of snore sounds, we estimate the TAR and the properties of the source excitation. 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 show that snore sounds provide an excellent framework for noncontact diagnosis of OSA suitable for development as a population mass screening technique.

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