The mean of peak frequencies, central frequencies, and proportions of energy from 800 Hz to 2000 Hz and above 2000 Hz of the first snoring sounds after lower level obstructive apneas were higher and the proportion of energy below 800 Hz was lower than those after upper level obstructive apneas. The differences of all the parameters were of significance. The power spectrum of the two types of snoring sounds also exhibited different characters.
Snore related signals (SRS) have been found to carry important information about the snore source and obstruction site in the upper airway of an Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) patient. An overnight audio recording of an individual subject is the preliminary and essential material for further study and diagnosis. Automatic detection, segmentation and classification of SRS from overnight audio recordings are significant in establishing a personal health database and in researching the area on a large scale. In this study, the authors focused on how to implement this intelligent method by combining acoustic signal processing with machine learning techniques. The authors proposed a systematic solution includes SRS events detection, classifier training, automatic segmentation and classification. An overnight audio recording of a severe OSAHS patient is taken as an example to demonstrate the feasibility of their method. Both the experimental data testing and subjective testing of 25 volunteers (17 males and 8 females) demonstrated that their method could be effective in automatic detection, segmentation and classification of the SRS from original audio recordings.
The LAeq and L10 in OSAHS patients were significantly different from patients with SS. The body mass index (BMI) was positively correlated to LAeq and L10. Among various factors of PSG data and demographic factors, the SPLs were mostly affected by the AHI and the lowest oxygen saturation (LSaO2).
Objective: To investigate the source of snoring sound in patients with simple snoring (SS) and different degrees of obstructive sleep apnea syndrome (OSAS) in order to provide a basis for the surgical treatment of snoring. Methods: Fifty-two patients with either SS or OSAS (with an apnea-hypopnea index ≤40) underwent drug-induced sleep nasendoscopy (DISN). Vibration sites in the pharyngeal cavity were observed. Results: Vibration of the soft palate, pharyngeal lateral wall, epiglottis, and tongue base appeared in 100, 53.8, 42.3, and 26.9% of the patients, respectively. The source of snoring sound was divided into two types: palatal fluttering only (type I) and multisite vibration (type II). The latter was divided into 3 subtypes: palatal fluttering with epiglottis vibration (type IIa), palatal fluttering with lateral wall vibration (type IIb), and palatal fluttering with vibration of the lateral wall, epiglottis, and tongue base together (type IIc). The distribution of type I snoring was the highest in SS patients. Type IIb was more common in patients with medium and severe OSAS. Type IIc was most common in patients with severe OSAS. Conclusion: The source of snoring sound is diverse, with SS and OSAS patients showing different features. DISN is a very effective method of identifying the snoring source.
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