Analysing of the snore sound signals with AutoRegressive modelling

Abstract

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The aim of this work is to study apnea, hypopnea and normal snoring sounds by using the criterias that are not used before in this area. The snoring sounds which are separated from segşments, that are in case of each inspiration and expiration, after enhanced by wavelet transform method. The AutoRegressive model order of these segments are determined with Final Prediction Error and Swartz Bayesion Criterion. Autocorrelation, Loss function and energy of segments are calculated on these sounds modelled with (AR) Autoregressive Model. The results were showed that the model order and energies of segments are the highest for patients of having apnea problem, middle degree for patients of having hypopnea problem and lowest degree for the patients of having normal snoring problems. In the meantime, loss function values were different for the patients of apnea, hypopnea and normal snoring patients. Data were obtained from Gulhane Military Medical Hospital at 20 patients. Those are 4 normal snoring, 8 hyponea problem and 8 apnea problem patients.