Pitch/airway-response and cepstral analysis of snore sounds for the non-contact screening of sleep apnea

Abstract

Snoring is the earliest and the most frequent symptom of Obstructive Sleep Apnea (OSA), which is a serious disease with a high community prevalence rate. The standard method of OSA diagnosis requires an overnight stay in a sleep lab, connected to over 15 channels of contact-measurements. There is evidence suggesting that Snore-Related-Sounds (SRS) carry sufficient information to diagnose OSA. Snore-based technology opens up opportunities for community-screening devices that do not depend on contact instrumentation. A new technique of diagnosing OSA based solely on multi-parametric snore sound analysis is presented in this paper. The method comprises of a logistic regression model fed with a range of snore parameters derived from its features, the pitch and the Total Airways Response (TAR) estimated using a Higher Order Statistics (HOS) based algorithm. The model was developed and its performance validated on a clinical database consisting of overnight snoring sounds of 41 subjects. Leaveone-out technique was used for validating the model. The validation process achieved 89.3% sensitivity with 92.3% specificity (area under the Receiver Operating Characteristic (ROC) curve was 0.96) in classifying the data sets into the two groups OSA and non-OSA. These results are superior to the existing results and unequivocally illustrate the feasibility of developing a snore-based OSA screening device.