Obstructive Sleep Apnea Screening Test Techniques Development using Analysis and Classification of Snoring Sound

Research Overview

Obstructive Sleep Apnea (OSA) is the most common type of sleep disorder and is caused by obstruction of the upper airway during sleep. This symptom arouses pauses in breathing which lead to deoxygenation and consequent arousals.

Figure 1. Obstructive sleep apnea (from wikipedia)

The effects of sleep disorders are extensive, impacting sufferers physically, psychologically and financially. Up to 40% of the US adults experience problems with falling asleep or daytime sleepiness. (Hossain and Shapiro 2002)

Figure 2. Effects of sleep disorders (from Ronald Kessler, Harvard Medical School 2006)

Moreover, diagnosis rate of OSA is very low on account of the diagnostic complexity and heavy expenditure although this sleep disorder has high fatality and prevalence.
Recently, interests about wearable device and telemedicine have been increasing and market requires high clinical accuracy in personalized handheld devices for mobile healthcare. Especially, OSA is typically cannot be aware of the disease in the single person households. So if we can develop simple and accurate algorithm which can be an OSA screening test, this is a good solution for national health promotion and creates values for next generation mobile healthcare system.

In our study, we only use snoring sound in the polysomnography, although other studies typically used various physiological parameters (oxygen saturation, body movement, EEG, EOG, EMG etc.). We extract various audio features in the perspectives of temporal structures and raw signal processing. Or we may conduct feature learning which is a set of techniques in machine learning that learn a transformation of raw inputs to a representation that can be effectively exploited in a supervised learning task such as classification.

Through this research, we may obtain good classification or regression models to inform the patients’ OSA status.

This research is underway. So, if we get a significant good performance, we can update this page immediately.

Project Members

Taehoon Kim, Yoonchang Han, Ziwon Hyung

Funding Agency

Seoul National University Hospital