All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome.

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h(-1)) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h(-1)) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h(-1)) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h(-1), respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Normal non-regular snores as a tool for screening SAHS severity.

Snoring is one of the earliest and most consistent sign of upper airway obstruction leading to Sleep Apnea-Hypopnea Syndrome (SAHS). Several studies on post-apneic snores, snores that are emitted immediately after an apnea, have already proven that this type of snoring is most distinct from that of normal snoring. However, post-apneic snores are more unlikely and sometimes even inexistent in simple snorers and mild SAHS subjects. In this work we address that issue by proposing the study of normal non-regular snores. They correspond to successive snores that are separated by normal breathing cycles. The results obtained establish the feasibility of acoustic parameters of normal non-regular snores as a promising tool for a prompt screening of SAHS severity.

Regular and non regular snore features as markers of SAHS.

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH(adaptive) and TH(median)) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index) < 5h(-1), AHI <10 h(-1), AHI < 15 h(-1), AHI < 30 h(-1)). Results showed that TH(adaptive) outperformed TH(median) on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

Variability of snore parameters in time and frequency domains in snoring subjects with and without Obstructive Sleep Apnea.

Several studies have shown differences in spectral parameters between simple snorers and patients with Obstructive Sleep Apnea Syndrome (OSAS). Most of them analyzed a reduced number of snores and/or only post-apneic snores were selected in OSAS patients. Previous findings suggest that snore parameters have a greater variability as the severity of OSAS increases. In this work we propose to analyze the snoring variability through the magnitude of the first difference of snore parameters. The technique is applied to long time sound recordings from 9 simple snorers (15795 snores) and 15 OSAS patients (19263 snores). The snores are characterized by their sound intensity an by a set of spectral parameters The variability of snore parameters is well correlated to OSAS severity (r>0.7) and is significantly greater in OSAS patients than in simple snorers (p<0.005). The results are similar when post-apneic snores are excluded from the analysis. Snoring subjects are classified with a logistic regression model, which is validated with the live-one-out method. The model is adjusted to correctly classify 100% of OSAS patients for screening purposes previously to enroll for a whole polysomnographic study.