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The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features. [ABSTRACT FROM PUBLISHER]

Summary: Age-specific and age-standardized associations between socioeconomic status (SES) and fractures in adults showed a social gradient of fracture, irrespective of fracture site. Compared to the highest SES, males in the lowest SES group had a sixfold increased odds for any fracture, whilst females had a twofold increased odds. Introduction: The effective identification of predisposing risk factors for fracture requires understanding any association with SES. These investigations should consider both sexes, span the adult age range and include any fractures. We investigated age- and sex-specific and age-standardized associations between SES and fractures at any skeletal site in Australians aged ≥50 years. Methods: Incident fractures that occurred 2006-2007 for adults aged ≥50 years were identified from radiological reports extracted for the Barwon Statistical Division, in south-eastern Australia. SES was determined by cross-referencing residential addresses with Australian Bureau of Statistics census data and then categorized in quintiles. We compared frequencies of observed vs. expected fractures for SES quintiles using χ comparison, calculated age-specific fracture incidence across SES and compared age-standardized fracture rates in SES quintile 1 to quintile 5. Results: We identified 3943 incident fractures (69.4 % female); 47.4 % had occurred at major osteoporotic fracture (MOF) sites (hip, humerus, spine and forearm/wrist). Differences existed in observed vs. expected fractures across SES quintiles ( p ≤ 0.001, sexes combined); all fractures showed an inverse association with SES ( p ≤ 0.001, sexes combined). Compared to the highest SES quintile, individuals from the lowest SES quintile had between two to six times greater standardized fracture rates. Conclusions: Disadvantaged men and women have an increased fracture incidence compared to their less disadvantaged counterparts. The large differences in fracture rates between SES groups warrant further research into designing appropriate, targeted interventions for those demographics at most risk. [ABSTRACT FROM AUTHOR]

FRAX evaluates 10-year fracture probabilities and can be calculated with and without bone mineral density (BMD). Low socioeconomic status (SES) may affect BMD, and is associated with increased fracture risk. Clinical risk factors differ by SES; however, it is unknown whether aninteraction exists between SES and FRAX determined with and without the BMD. From the Geelong Osteoporosis Study, we drew 819 females aged ≥50 years. Clinical data were collected during 1993-1997. SES was determined by cross-referencing residential addresses with Australian Bureau of Statistics census data and categorized in quintiles. BMD was measured by dual energy X-ray absorptiometry at the same time as other clinical data were collected. Ten-year fracture probabilities were calculated using FRAX (Australia). Using multivariable regression analyses, we examined whether interactions existed between SES and 10-year probability for hip and any major osteoporotic fracture (MOF) defined by use of FRAX with and without BMD. We observed a trend for a SES * FRAX interaction term for 10-year hip fracture probability ( p = 0.09); however, not for MOF ( p = 0.42). In women without prior fracture ( n = 518), we observed a significant SES * FRAX interaction term for hip fracture ( p = 0.03) and MOF ( p = 0.04). SES does not appear to have an interaction with 10-year fracture probabilities determined by FRAX with and without BMD in women with previous fracture; however, it does appear to exist for those without previous fracture. [ABSTRACT FROM AUTHOR]