Neural networks for snore sound modeling in sleep apnea

Snoring is the earliest and the most common symptom of Obstructive Sleep Apnea (OSA) which is a serious disease caused by the collapse of upper airways during sleep. Recently, a few pioneering attempts have been made to use snore sounds (SS) Is diagnosing OSA. The SS are simple to acquire and rich in features but their analysis is complicated. In this paper, we propose a neural network (NN) based method to model SS via a technique associated with k-step prediction. We also show that the features of a SS can be conveniently captured in the connection-weight-space (CWS) of the NN, after a process of supervised training. The performance of the proposed method is investigated via simulated and clinically measured data.