An efficient fast method of snore detection for sleep disorder investigation

Çavuşoğlu, Mustafa
Snores are breath sounds that most people produce during sleep and they are reported to be a risk factor for various sleep disorders, such as obstructive sleep apnea syndrome (OSAS). Diagnosis of sleep disorders relies on the expertise of the clinician that inspects whole night polysomnography recordings. This inspection is time consuming and uncomfortable for the patient. There are surgical and therapeutic treatments. However, evaluation of the success of these methods also relies on subjective criteria and the expertise of the clinician. Thus, there is a strong need for a tool to analyze the snore sounds automatically, and to produce objective criteria and to assess the success of the applied treatment by comparing these criteria obtained before and after the treatment. In this thesis, we proposed a new algorithm to detect snoring episodes from the sleep sound recordings of the individuals, and created a user friendly interface to process snore recordings and to produce simple objective criteria to evaluate the results. The algorithm classifies sleep sound segments as snores and nonsnores according to their subband energy distributions. It was observed that inter- and intra-individual spectral energy distributions of snore sounds show significant similarities. This observation motivated the representation of the feature vectors in a lower dimensional space which was achieved using principal component analysis. Sleep sounds can be efficiently represented and classified as snore or nonsnore in a two dimensional space. The sound recordings were taken from patients that are suspected of OSAS pathology while they were connected to the polysomnography in Gülhane Military Medical Academy Sleep Studies Laboratory. The episodes taken from 30 subjects (18 simple snorers and 12 OSA patients) with different apnea/hypopnea indices were classified using the proposed algorithm. The system was tested by using the manual annotations of an ENT specialist as a reference. The system produced high detection rates both in simple snorers and OSA patients.
Citation Formats
M. Çavuşoğlu, “An efficient fast method of snore detection for sleep disorder investigation,” M.S. - Master of Science, Middle East Technical University, 2007.