Snore site discrimination using statistical moments of free field snoring sounds recorded during sleep nasendoscopy

Abstract

The non-invasive measurement of free field snoring sounds to estimate the site of snoring is an important development in the diagnosis, treatment and management of sleep-related breathing disorders. We investigated characteristics of the probability density function by testing the sensitivity of the statistical moments to amplitude patterns in the snoring acoustic signal. Snoring sounds from 15 patients were recorded whilst performing sleep (under anaesthetic) nasendoscopy evaluation. We demonstrated, using a fuzzy 2-means clustering method, that a combination of the statistical dimensionless moment coefficients of skewness and kurtosis could discriminate between pure palatal and non-palatal snoring subject groups.

Epiglottis collapse in adult obstructive sleep apnea: A systematic review

“…Beeton et al proposed that vibratory forces produced at the level of the tongue base and the epiglottis experience low-frequency absorption. 38 Agrawal et al performed sound frequency analysis of different snoring sites and found that epiglottis snoring has a peak frequency of 490 hertz (Hz), whereas tonsillar vibrations have a peak frequency of 170 Hz. 39 Won et al observed that there is an increase in pitch frequency in tongue base and epiglottis snoring compared with soft palate snoring.…”

Evaluation of acoustic characteristics of snoring sounds obtained during drug-induced sleep endoscopy

“…Thereby, a possible differentiation between palatal and non-palatal snoring is frequently reported. Contrary to that, other sites of obstruction can hardly be detected by acoustic analyses [4,1,7,[22][23][24][25].…”

Ensemble Heterogeneous Feature Selection (EHFS) and Heterogeneous Ensemble Classifier with VOTE (HECV) for Automatic Detection of Snore Sounds

“…Kim et al [19] aimed to identify acoustic biomarkers indicative of the severity of sleep disordered breathing (SDB) by analyzing the breathing sounds collected from a large number of subjects during entire overnight sleep. Next the audio features were collected and a set of features that vary between different SDB severity groups was identified as a potential acoustic biomarker.…”