Please login first
A Real-Time Snore Detector Using Neural Networks and Selected Sound Features
* , , , , *
1  Department of Electrical and Electronics Engineering, University of West Attica, Athens, GREECE
Academic Editor: Roger Narayan (registering DOI)

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic condition held responsible for a number of well-documented effects on patients’ health. It is linked to increased cardiovascular morbidity and mortality, including sudden heart death [1], while an estimated 4% and 2% of the male and female population respectively suffer from OSAHS. Interestingly enough, an estimated 85% of patients remain undiagnosed [2]. This underestimation poses an increased risk for individuals and society as a whole and is mainly due to polysomnography being the only method for OSAHS diagnosis currently trusted by doctors. Polysomnography is a time and resource-consuming procedure that monitors sleep with a multitude of specialized sensors and equipment and is performed in dedicated sleep laboratories or hospital care clinics. As such, most of the suffering population remains unscreened and, hence, undiagnosed.

Screening the disease’s symptoms at home could be used as an alternative approach in order to alert individuals that potentially suffer from OSAHS without compromising their everyday routine. Since snoring is usually linked to OSAHS, developing a snore detector is appealing as an enabling technology for screening OSAHS at home using ubiquitous equipment like commodity microphones (included in, e.g., smartphones). Within the context of the APNEA research project [3], we developed a snore detection tool and herein present our approach and selection of specific sound features that discriminate snoring vs. environmental sounds, as well as the performance of the proposed tool. Furthermore, a Real-Time Snore Detector (RTSD) is built upon the snore detection tool and employed in whole-night sleep sound recordings resulting to a large dataset of snoring sound excerpts that are made freely available to the public. The RTSD may be used either as a stand-alone tool that offers insight to an individual’s sleep quality or as an independent component of OSAHS screening applications in future developments.

[1] Qaseem, A., et al., “Management of obstructive sleep apnea in adults: a clinical practice guideline from the American College of Physicians”, Annals of Internal Medicine, October 2013, DOI: 10.7326/0003-4819-159-7-201310010-00704.

[2] Pack, A. I., “Advances in sleep-disordered breathing”, American Journal of Respiratory and Critical Care Medicine, January 2006, DOI: 10.1164/rccm.200509-1478OE.

[3] APNEA Project: “Automatic pre-hospital and in-home screening of sleep apnea”, Operational Programme “Competitiveness, Entrepreneurship and Innovation”, website: (accessed on April, 01, 2021).

Keywords: Obstructive sleep apnea hypopnea syndrome; apnea screening; snoring detection; machine learning; neural networks.