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Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.

BACKGROUND:The sleep apnea-hypopnea syndrome is associated with elevated oxidative stress, which is associated with reduced levels and functional impairment of progenitor cells. OBJEC-TIVE: To evaluate whether one month of CPAP treatment affects circulating-progenitor-cell levels and oxidative stress in patients with sleep apnea-hypopnea syndrome. METHODS: We enrolled 13 patients with sleep apnea-hypopnea syndrome who required nasal CPAP. We evaluated whiteblood-cell oxidative stress and CD45؊, CD34؉, KDR؉, and CD133؉ cell levels via flow-cytometry, before and one month after CPAP treatment. RESULTS: Superoxide anion and hydrogen peroxide were reduced, and markers of protection against oxidative stress were increased after CPAP. Progenitor-cell levels increased significantly after CPAP. There was a significant negative correlation between CD45؊, CD34؉, KDR؉, and CD133؉ cell levels and the severity of sleep apneahypopnea syndrome and superoxide anion. CONCLUSIONS: CD45؊, CD34؉, KDR؉, and CD133؉ cell levels rose significantly and reached values close to those in the control group after one month of CPAP. This change was accompanied by a significant decrease in oxidative stress, and no change in anthropometric or metabolic variables, including insulin resistance, weight, blood pressure, or lipid levels; consequently, the increase in progenitor-cell levels might be attributable to reduced oxidative stress.

BackgroundSleep 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.MethodsA 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-the-art 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.ResultsThe 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.ConclusionThe 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.

Sleep apnea-hypopnea syndrome (SAHS) is characterized by recurrent episodes of hypoxia/reoxygenation, which seems to promote oxidative stress. SAHS patients experience increases in hypertension, obesity and insulin resistance (IR). The purpose was to evaluate in SAHS patients the effects of 1 month of treatment with continuous positive airway pressure (CPAP) on oxidative stress and the association between oxidative stress and insulin resistance and blood pressure (BP). Twenty-six SAHS patients requiring CPAP were enrolled. Measurements were recorded before and 1 month after treatment. Cellular oxidative stress parameters were notably decreased after CPAP. Intracellular glutathione and mitochondrial membrane potential increased significantly. Also, total antioxidant capacity and most of the plasma antioxidant activities increased significantly. Significant decreases were seen in BP. Negative correlations were observed between SAHS severity and markers of protection against oxidative stress. BP correlated with oxidative stress markers. In conclusion, we observed an obvious improvement in oxidative stress and found that it was accompanied by an evident decrease in BP with no modification in IR. Consequently, we believe that the decrease in oxidative stress after 1 month of CPAP treatment in these patients is not contributing much to IR genesis, though it could be related to the hypertension etiology.