Publications

The snore is a breathing sound that originates during sleep, either nocturnal or diurnal. Many procedures have been used for its analysis, from simple interrogation, going through acoustic methods that have been developed thanks to the advance of biomedical techniques in recent years. So far a procedure homologated by different laboratories for its study doesn’t exist. The present editorial describes the current state of the art in the snoring analysis procedures.

JTD Keywords: Snoring, Sleep apnea, OSAS

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

JTD Keywords: Bayes classifier, Kernel density estimation, Sleep apnoea, Snoring

El ronquido es un sonido respiratorio que se produce durante el sueño, ya sea nocturno o diurno. El ronquido puede ser inspiratorio, espiratorio o puede ocupar todo el ciclo respiratorio. Tiene su origen en la vibración de los diferentes tejidos de la vía aérea superior. Se han descrito numerosos métodos para analizarlo, desde el simple interrogatorio, pasando por cuestionarios estándares, hasta llegar a los métodos acústicos más sofisticados, que se han desarrollado gracias al gran avance de las técnicas biomédicas en los últimos años. El presente trabajo describe el estado del arte actual en los procedimientos de análisis del ronquido.

JTD Keywords: Ronquido, Apnea del sueño, Síndrome de apnea-hipoapnea del sueño, Snoring, Sleep apnea, Sleep Apnea and Hipoapnea Syndrome

Objectives/Hypothesis: We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI Study Design: Individual Case-Control Study. Methods: Thirty-seven snorers (12 females and 25 males, ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >= 5, <15) and G3 (AHI >= 15) Snore and breathing sounds were : recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model. Results: There were significant differences in supine position between G1 and G3 in sound intensity, number of snores; standard deviation of the spectrum, power ratio in bands 0-500, 100-500, and 0-800 Hz, and the symmetry coefficient (P < .03); Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively. Conclusions: A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

JTD Keywords: Breathing sounds, Signal interpretation, Sleep apnea syndromes, Snoring

Management of snoring is part of the treatment offered to patients with obstructive sleep apnea syndrome. In patients who do not have this syndrome, however, snoring should be treated according to the severity of the condition. General or specific therapeutic measures should be applied to snorers that have concomitant cardiovascular disease or unrefreshing sleep and in cases in which an individual's snoring disturbs his/her partner's sleep. The present review examines the treatments currently available for snorers and the current state of knowledge regarding each option. It will also focus on the possible indications of these treatments and evaluate their effectiveness.

JTD Keywords: Simple snoring, Treatment, General measures, Surgery