Analysed snoring sounds correlate to obstructive sleep disordered breathing

Abstract

Snoring occurs as a major symptom in patients with sleep disordered breathing (SDB). The aetiology of snoring remains still unclear despite various attempts to localize snoring. The correlation between different snoring sounds and the severity of SDB has not yet been investigated in a larger population. The aim of this study was to record and analyse snoring sounds and to correlate the obtained data with clinical and polysomnographical parameters. Sixty male patients with suspected SDB and reported snoring underwent a clinical examination and night time polysomnography. The parallel digitally recorded snoring sounds were analysed by fast fourier transformation (FFT). Peak intensity was determined from the power spectrum. The periodicity of snoring was classified into rhythmic and non-rhythmic snoring according to the presence of air flow interruptions due to obstructive apneas. Patients with primary snoring revealed peak intensities between 100 and 300 Hz. Patients with an obstructive sleep apnea syndrome (OSAS) revealed peak intensities above 1,000 Hz. Polysomnographical data (AHI, mean and minimum SpO(2)) as well as body mass index (BMI) correlated with peak intensity of the power spectrum. None of the parameters of the clinical examination correlated with peak intensity. Frequency analysis of snoring sounds provides a useful diagnostic tool to distinguish between different patterns of snoring and respective SDB. The topodiagnosis of snoring is not possible by means of frequency analysis or clinical examination alone. Acoustical analysis of snoring sounds seems a promising additional diagnostic tool to verify different types of SDB in snoring patients.

Evaluation of the different sleep-disordered breathing patterns of the compressed tracheal sound

“…The assumption that the higher tracheal sound frequencies represent obstruction is supported by earlier studies (Rao et al, 1990;Yonemaru et al, 1993;Pasterkamp et al, 1996;Kaniusas et al, 2005;Herzog et al, 2008;Michael et al, 2008). Here we present that the thin pattern contained tracheal sound components of a higher amplitude than the plain and the thick pattern.…”

“…Our hypothesis is that during the plain sound pattern the oesophageal pressure (pESO) values remain normal and that during the thick and the thin patterns, the pESO negativity increases indicating upper airway obstruction. As along with increasing obstruction, the spectrum of the tracheal sound signal consists more of higher frequencies (Rao et al, 1990;Yonemaru et al, 1993;Pasterkamp et al, 1996;Kaniusas et al, 2005;Herzog et al, 2008;Michael et al, 2008), we hope to find out that during the plain sound pattern, the spectrum of the tracheal sound consists mostly of lower frequencies. During the thick and the thin patterns higher frequencies would be more common.…”

The annoyance of snoring and psychoacoustic parameters: a step towards an objective measurement

“…It is reported in the literature that obstructive sounds, which are mainly caused around the base of the tongue, have their peak intensities above 1,000 Hz, whereas habitual snoring sounds tend to arise in the area of the palate and display peak intensities between 100 and 300 Hz [12,24]. DiVerences also exist in terms of the presence of harmonics, the mean snoring intensity and psychoacoustic parameters [25][26][27].…”

“…Nevertheless, in order to make an assessment of the degree of annoyance or changes achieved by a therapeutic measure, one of the following approaches is usually adopted: a subjective assessment using questionnaires or visual analog scales is carried out by patient, bed partner or examiner [8][9][10]; or a more objective physical evaluation is done, in which parameters such as duration, frequency, acoustic frequencies, sound pressure level or energy ratios are utilized and compared [11][12][13].…”

Role of Upper Airway Dimensions in Snore Production: Acoustical and Perceptual Findings

“…10,30 In recent years, multiple acoustic markers of snore signals have been proposed to discriminate between apneic and benign patients, with a common interest to develop a non-invasive, inexpensive, and rapid screening tool for OSA. These markers include, but are not limited to, first formant frequency (F1) in the linear prediction (LP) spectrum 30 ; peak frequency (PF) in the fast Fourier transformation curve 25 ; frequency modes in the bispectrum 32 ; mean and standard deviation of the coefficient of variation in a short signal frame 7 ; soft phonation index and noise-to-harmonics ratio 17 ; and even psychoacoustic metrics in terms of loudness, sharpness, roughness, fluctuation strength, and annoyance. 29 Although the snore-driven markers appear to shed light on OSA detection, there is little research on correlation between the UA dimensions and the properties of snores.…”