Automatic Detection of Snore Episodes in Paediatric Population

Cavusoglu M., Burger H. C. , Brockmann P. E. , Poets C. F. , Urschitz M. S. , Kamaşak M. E. , ...More

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 23 - 25 April 2014, pp.1138-1141 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Trabzon
  • Country: Turkey
  • Page Numbers: pp.1138-1141


In this paper, a novel algorithm is proposed for automatic detection of snoring sounds from ambient acoustic data in a pediatric population. With the approval of institutional ethic committee and parents, the respiratory sounds of 50 subjects were recorded by using a pair of microphones and multi-channel data acquisition system simultaneously with full-night polysomnography during sleep. Brief sound chunks of 0.5 s were classified as either belonging to a snoring event or not with a multi-layer perceptron which was trained in a supervised fashion using stochastic gradient descent on a large hand-labeled dataset using frequency domain features. The overall accuracy of the proposed algorithm was found to be 88.93% for primary snorers and 80.6% for obstructive sleep apnea (OSA) patients.