Since the 1970s, various automatic sleep spindles procedures have been implemented and presented in the literature. Unfortunately, their results are not easily comparable because the databases, the assessment methods and the terminologies employed are often radically different. In this study, we propose a systematic assessment method for any automatic sleep spindles detection algorithm. We apply this assessment method to our own automatic detection process in order to illustrate and legitimate its use. We obtain a global sensitivity of 70.20%, for a false positive proportion (relative to the total number of visually scored sleep spindles) of only 26.44% (False positive rate = 1.38% and specificity = 98.62%).

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Objectives: This study examines the effects of sleep restricted to four hours for three consecutive nights on blood parameters, known to be associated with cardiovascular risk, in young healthy men. Material and methods: Eight young healthy men (age 24.5 ± 3.3 years) were studied in the sleep restricted group. Nine young healthy men (age 24 ± 2 years) were included in the control group and spent the days and nights in the sleep lab, while sleeping eight hours/night. One baseline night was followed by three nights of sleep restriction to four hours and by one recovery night of eight hours. Blood samplings were performed after the baseline night and after the third night of sleep restriction or without restriction for the control group. Results: A signifi cant increase in white blood cells (WBC) (5.79 ± 1.05 vs. 6.89 ± 1.31 10 3 cell/μl, p = 0.03), and neutrophils (3.17 ± 0.69 vs 4.24 ± 0.97 10 3 cell/μl, p = 0.01) was observed after the third night of sleep restriction. Other blood parameters were not affected. No signifi cant variation was observed in the control group. Conclusion: Sleep restriction affected WBC count, mainly neutrophils, considered as risk factor for cardiovascular disease. Stress induced by the short term sleep restriction could be involved in this observation.

Abstract-In this paper, we present an automatic method for K-complexes detection based on features extraction and the use of fuzzy thresholds. The validity of our process was examined on the basis of two visual K-complexes scorings performed on 5 excerpts of 30 minutes. Results were investigated through all different sleep stages. The algorithm provides global true positive rates of 61.72% and 60.94%, respectively with scorer 1 and scorer 2. The false positive proportions (compared to the total number of visually scored K-complexes) are of 19.62% and 181.25%, while the false positive rates estimated on a one 1 second resolution are only of 0.53% and 1.53%. These results suggest that our approach is completely suitable since its performances are similar to those of the human scorers.