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.


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Obstructive Sleep Apnea Syndrome (OSAS) is defined as a sleep related breathing disorder that causes the body to stop breathing for about 10 seconds and mostly ends with a loud sound due to the opening of the airway. OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, with multiple electrodes attached to the patient's body. Snoring is a symptom which may indicate presence of OSAS; thus investigation of snoring sounds, which can be r...
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Obstructive sleep apnea (OSA) is a sleep disorder with long-term adverse effects such as cardiovascular diseases. However, clinical methods, such as polisomnograms, have high monitoring costs due to long waiting times and hence efficient computer-based methods are needed for diagnosing OSA. In this study, we propose a method based on feature selection of fused oxygen saturation and electrocardiogram signals for OSA classification. Specifically, we use Relieff feature selection algorithm to obtain robust fea...
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In recent years, several studies have shown the relationship between snoring and obstructive sleep apnea syndrome (OSAS). Besides time domain analysis of snoring signal, the spectral features and shapes of snores can be used to discriminate simple snorers and OSAS patients. In this study, we propose a method to classify simple snorers and OSAS patients based on spectral envelope estimation of snoring signals. The formant frequencies and corresponding bandwidths are computed for both group, and the variation...
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Sleep spindles are one of the rhythmic activities observed in sleep electroencephalogram (EEG). As they are well defined and functional, sleep spindle analysis is significant for brain research. Identifying the characteristics of sleep spindles may lead to an understanding of the functions of sleep. Furthermore, understanding the sleep spindle generation mechanisms can explain the other rhythmical activity occurring in other brain regions. The detection process of the sleep spindle data of a whole night sle...
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.