Acoustic analysis of snoring sounds recorded with a smartphone according to obstruction site in OSAS patients

Abstract

Snoring is a sign of increased upper airway resistance and is the most common symptom suggestive of obstructive sleep apnea. Acoustic analysis of snoring sounds is a non-invasive diagnostic technique and may provide a screening test that can determine the location of obstruction sites. We recorded snoring sounds according to obstruction level, measured by DISE, using a smartphone and focused on the analysis of formant frequencies. The study group comprised 32 male patients (mean age 42.9 years). The spectrogram pattern, intensity (dB), fundamental frequencies (F ), and formant frequencies (F, F , and F) of the snoring sounds were analyzed for each subject. On spectrographic analysis, retropalatal level obstruction tended to produce sharp and regular peaks, while retrolingual level obstruction tended to show peaks with a gradual onset and decay. On formant frequency analysis, F (retropalatal level vs. retrolingual level: 488.1 ± 125.8 vs. 634.7 ± 196.6 Hz) and F (retropalatal level vs. retrolingual level: 1267.3 ± 306.6 vs. 1723.7 ± 550.0 Hz) of retrolingual level obstructions showed significantly higher values than retropalatal level obstruction (p < 0.05). This suggests that the upper airway is more severely obstructed with retrolingual level obstruction and that there is a greater change in tongue position. Acoustic analysis of snoring is a non-invasive diagnostic technique that can be easily applied at a relatively low cost. The analysis of formant frequencies will be a useful screening test for the prediction of occlusion sites. Moreover, smartphone can be effective for recording snoring sounds.

The Effect of Adenotonsillectomy on Korean Children’s Voice

“…Previous research has found that variations in F1 reflect airway changes in apneic snorers, whereas variations in F2 reflect changes in the morphology of the tongue and variations in F3 reflect lip movements. [11][12][13] Some recent studies have used objective analysis of formants as well as subjective analyses. Some have reported that F0, F1, and F2 formant frequencies do not change significantly after tonsillectomy with or without adenoidectomy.…”

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography

“…Future studies should determine if abnormal breathing events may be missed by snore analysis alone (i.e., reduced sensitivity) or if normal snores in individuals without SDB are inadvertently captured (i.e., reduced specificity). Koo et al analyzed smartphone recordings during drug-induced sleep endoscopy [26] using spectral analysis to identify the pharyngeal region of obstruction. However, sounds during drug-induced sleep endoscopy may differ from natural at-home sleep.…”

“…Recording sound from an external microphone, Ben-Israel et al developed Gaussian mixture models to detect snore, noise, and silence events with 92% sensitivity for OSA (AHI ≥ 10), and identified novel acoustic features of sleep-breathing including silence, stability, and variance of sounds and pitch [31]. Acoustic analyses may ultimately identify the level of airway obstruction [26] or airflow phenotypes associated with facial structure [32]. Clinically, acoustic screening of SDB should be tested as a gateway to definitive diagnosis by PSG or HST in diverse scenarios, to ensure that algorithms are robust to noise, varying distance of the phone from the individual, and other nocturnal variations in breathing.…”

Deep Learning Features for Robust Detection of Acoustic Events in Sleep-disordered Breathing

“…Beeston was funded by KTP award 9905 with PFLH. a convenient, non-invasive, and low-cost method to diagnose SDB through acoustic analysis [12].…”