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Obstructive Sleep Apnea Syndrome (OSAS) is defined as a sleep related breathing disorder that causes the body to stop breathing for about 10 seconds and mostly ends with a loud sound due to the opening of the airway. OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, with multiple electrodes attached to the patient's body. Snoring is a symptom which may indicate the presence of OSAS; thus investigation of snoring sounds, which can be recorded in the patient's own sleeping environment, has become popular in recent years to diagnose OSAS. In this study, we aim to develop a new method to detect post-apnea snoring episodes with the goal of diagnosing apnea or creating new criteria similar to apnea / hypopnea index. Emphasis is placed on detecting post apnea episodes, hence the apnea periods. In this method, first segmentation is done to eliminate the silence parts. Then, these episodes are represented by distinctive features; some of these features are available in literature but some of them are novel. Finally, episodes are classified using supervised methods. False alarm rates are reduced by adding additional constraints into the detection algorithm. These methods are applied to snoring sound signals of OSAS patients, recorded in Gulhane Military Medical Academy, to verify the success of our algorithms.

As technology advances, practicing or training otolaryngologists register in educational courses to refresh their surgical skills and stay abreast of these changes. While information is becoming available in several journals, there is a definite need for a single surgical atlas-like book that contains precise descriptions of the surgical techniques used in rhinology and sleep apnea surgery. Each chapter was written by surgeons with extensive experience in each topic who have already published on the specific procedure described. Each chapter contains diagrammatic or illustrative descriptions of surgical techniques and provides tips and pearls of wisdom to avoid complications when the procedures are performed. This comprehensive work of surgical information serves as a valuable resource for otolaryngologists in training to augment their surgical education and for practicing otolaryngologists as a review source to best approach the surgical pathologies they encounter in their practice of rhinology and treatment of sleep apnea.

Obstructive sleep apnea (OSA) is characterized by nocturnal collapsing of the upper airways. Consequently complete cessation of breathing or reduced breathing phases appears. OSA is a widespread disorder affecting up to 11% of the male and up to 6% of the female population. It is associated with serious consequences such as myocardial infarction, stroke, hypertension and traffic accidents. Nasal continuous positive airway pressure (nCPAP) ventilation is the gold standard in the treatment of obstructive sleep apnea syndrome (OSAS). Long-term compliance rates do not exceed more than 60 to 70%. Other options like surgical procedure exist. But only one surgical procedure won´t be successful in cases of moderate and severe OSA because one surgery will enlarge the airway only at one location. Alternative multi-level surgeries are of interest, combining procedures at the level of the base of tongue and the soft palate in order to stabilize the whole upper airway like the CPAP-ventilation. Several multi-level surgery concepts exist. Our multi-level surgery based on the hyoid suspension with the combination of a radiofrequency therapy of the tongue base brings out the effectiveness of this concept. With this concept we achieve a success rate of 57.6%; this result situates us at the average level of the cited multi-level surgery studies. With this success rate this protocol can replace the CPAP mask especially in cases with CPAP intolerance or decline.

This article discusses the limits of deep breath-hold diving in humans. After a short historical introduction and a discussion of the evolution of depth records, the classical theories of breath-hold diving limits are presented and discussed, namely that of the ratio between total lung capacity and residual volume and that of blood shift, implying an increase in central blood volume. Then the current vision is introduced, based on the principles of the energetics of muscular exercise. The new vision has turned the classical vision upside down, moving the discussion to a different level. A direct consequence of the new theory is the importance of having large lung volumes at the start of a dive, in order to increase body oxygen stores. I finally discuss the role of anaerobic lactic metabolism as a possible mechanism of oxygen preservation, thus prolonging breath-hold duration.

The electronic version of this article is the complete one and can be found online at: http://link.springer.com/article/10.1186/s12938-016-0138-5 ; Background: Sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). The altered UA structure or function in OSA speakers has led to hypothesize the automatic analysis of speech for OSA assessment. In this paper we critically review several approaches using speech analysis and machine learning techniques for OSA detection, and discuss the limitations that can arise when using machine learning techniques for diagnostic applications. Methods: A large speech database including 426 male Spanish speakers suspected to suffer OSA and derived to a sleep disorders unit was used to study the clinical validity of several proposals using machine learning techniques to predict the apnea–hypopnea index (AHI) or classify individuals according to their OSA severity. AHI describes the severity of patients' condition. We first evaluate AHI prediction using state-of-theart speaker recognition technologies: speech spectral information is modelled using supervectors or i-vectors techniques, and AHI is predicted through support vector regression (SVR). Using the same database we then critically review several OSA classification approaches previously proposed. The influence and possible interference of other clinical variables or characteristics available for our OSA population: age, height, weight, body mass index, and cervical perimeter, are also studied. Results: The poor results obtained when estimating AHI using supervectors or i-vectors followed by SVR contrast with the positive results reported by previous research. This fact prompted us to a careful review of these approaches, also testing some reported results over our database. Several methodological limitations and deficiencies were detected that may have led to overoptimistic results. Conclusion: The methodological deficiencies observed after critically reviewing previous research can be relevant examples of potential pitfalls when using machine learning techniques for diagnostic applications. We have found two common limitations that can explain the likelihood of false discovery in previous research: (1) the use of prediction models derived from sources, such as speech, which are also correlated with other patient characteristics (age, height, sex,…) that act as confounding factors; and (2) overfitting of feature selection and validation methods when working with a high number of variables compared to the number of cases. We hope this study could not only be a useful example of relevant issues when using machine learning for medical diagnosis, but it will also help in guiding further research on the connection between speech and OSA. ; Authors thank to Sonia Martinez Diaz for her effort in collecting the OSA database that is used in this study. This research was partly supported by the Ministry of Economy and Competitiveness of Spain and the European Union (FEDER) under project "CMC-V2", TEC2012-37585-C02.

Purpose Understanding disease prevalence can inform treatment and resource needs across populations. This study aimed to identify the prevalence of sleep apnea (OSA) among veterans and nonveterans. Design The national Comparative Health Assessment Interview Study, cross-sectional survey using probability-based sampling frames. Setting Surveys completed by Internet or phone. Subjects 15,166 veterans (40% response rate) and 4,654 nonveterans (57% response rate). Measures Self-report of healthcare provider-based diagnosis of OSA. Analysis Calculation of prevalence of OSA using statistical weighting to allow for direct comparison between veterans and nonveterans. Secondary analyses evaluated OSA by deployment status among veterans and compared average age of OSA diagnosis and differences in OSA prevalence among veterans and nonveterans stratified by gender, marital status, race/ethnicity, and posttraumatic stress disorder diagnosis. Results OSA diagnosis was more than twice as prevalent among veterans (21%, 95% CI 20%-22%) than nonveterans (9%, 95% CI 8%-10%; aOR: 2.56, 95% CI 2.22-2.95, P < .001). Deployment was associated with higher odds of OSA among veterans (aOR: 1.64, 95% CI 1.43-18.7, P < 001.) Veterans were diagnosed with OSA on average 5 years earlier than nonveterans. Conclusion Veterans have a high prevalence rate of OSA, highlighting the importance of veterans' access to treatment. OSA is likely underdiagnosed in nonveterans, particularly among racial/ethnic minoritized groups. Future research should investigate disparities in access to diagnostic testing for racial/ethnic minority nonveterans and/or risk factors for OSA among racial/ethnic minority veterans. The increased odds of OSA among those with PTSD highlights in the importance of early referral for OSA testing by providers as well as development of trauma-informed strategies to promote OSA treatment adherence. Limitations include a bias toward underestimation of true disease prevalence due to self-report of diagnosis.