Acoustics of snoring and automatic snore sound detection in children
M Çavuşoğlu1, C F Poets2, M S Urschitz2,3
1Institute for Biomedical Engineering, ETH Zurich, Gloriastr. 35, 8092 Zurich, Switzerland.
Insights
This study reveals distinct acoustic properties in children's snoring compared to adults, leading to a new algorithm for automatic snoring detection in pediatric patients. This aids in objective snoring evaluation for clinical use.
Area of Science:
- Biomedical Engineering
- Acoustic Analysis
- Sleep Medicine
Background:
- Objective assessment of snoring in adults utilizes automated acoustic analysis tools.
- Existing adult-focused methods are unsuitable for pediatric snoring due to differing acoustic properties.
- Accurate analysis of pediatric snoring requires specialized tools and understanding of unique sound characteristics.
Purpose of the Study:
- To characterize acoustic differences in snoring sounds between children and adults.
- To develop and validate a novel algorithm for automatic snoring detection in pediatric populations.
- To establish objective snore-related statistics for clinical application in children.
Main Methods:
- Calculated acoustic features including frequency domain, spectrograms, spectral envelope, formant structures, and loudness.
- Recorded respiratory sounds using microphones and polysomnography.
- Developed a multi-layer perceptron algorithm trained with stochastic gradient descent for snoring event detection.
Main Results:
- Significant differences observed in spectral features, formant structures, and loudness between pediatric and adult snoring sounds.
- Proposed a novel algorithm for automatic detection of snoring in children based on specific audio features.
- Algorithm achieved accurate classification of snoring events using frequency domain features.
Conclusions:
- The developed method enables extraction of comprehensive snore-related statistics from overnight recordings.
- These statistics include total snoring time, snore-to-sleep ratio, and snoring amplitude/time regularity.
- The findings provide a foundation for an objective clinical tool for evaluating pediatric snoring.
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.
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