Acoustics of snoring and automatic snore sound detection in children. (31st October 2017)

• Record Type: Journal Article
• Authors: Çavuşoğlu, M; Poets, C F; Urschitz, M S
• Is Part Of: Physiological measurement. Volume 38:Number 11(2017:Nov.)
• Page Start: 1919
• Page End: 1938
• Publication Date: 2017-10-31
• Subjects: snoring -- acoustic -- audio properties -- detection -- children; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aa8a39
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective : Acoustic analyses of snoring sounds have been used to objectively assess snoring and applied in various clinical problems for adult patients. Such studies require highly automatized tools to analyze the sound recordings of the whole night's sleep, in order to extract clinically relevant snore- related statistics. The existing techniques and software used for adults are not efficiently applicable to snoring sounds in children, basically because of different acoustic signal properties. In this paper, we present a broad range of acoustic characteristics of snoring sounds in children ( N = 38) in comparison to adult ( N = 30) patients. Approach : Acoustic characteristics of the signals were calculated, including frequency domain representations, spectrogram-based characteristics, spectral envelope analysis, formant structures and loudness of the snoring sounds. Main results : We observed significant differences in spectral features, formant structures and loudness of the snoring signals of children compared to adults that may arise from the diversity of the upper airway anatomy as the principal determinant of the snore sound generation mechanism. Furthermore, based on the specific audio features of snoring children, we proposed a novel algorithm for the automatic detection of snoring sounds from ambient acoustic data specifically in a pediatric population. The respiratory sounds were recorded using a pair of microphones and a 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. Significance : The method proposed here has been used to extract snore-related statistics that can be calculated from the detected snore episodes for the whole night's sleep, including number of snore episodes (total snoring time), ratio of snore to whole sleep time, variation of snoring rate, regularity of snoring episodes in time and amplitude and snore loudness. These statistics will ultimately serve as a clinical tool providing information for the objective evaluation of snoring for several clinical applications.