Pierre Maquet1,2,6, Steven Laureys1,2, Philippe Peigneux1,2,3, Sonia Fuchs1, Christophe Petiau1, Christophe Phillips1,6, Joel Aerts1, Guy Del Fiore1, Christian Degueldre1, Thierry Meulemans3, André Luxen1, Georges Franck1,2, Martial Van Der Linden3, Carlyle Smith4 and Axel Cleeremans5.

Sleep spindles are frequently studied for their relationship with state and trait cognitive variables, and they are thought to play an important role in sleep-related memory consolidation. Due to their frequent occurrence in NREM sleep, the detection of sleep spindles is only feasible using automatic algorithms, of which a large number is available. We compared subject averages of the spindle parameters computed by a fixed frequency (11-13 Hz for slow spindles, 13-15 Hz for fast spindles) automatic detection algorithm and the (...) individual adjustment method (IAM), which uses individual frequency bands for sleep spindle detection. Fast spindle duration and amplitude are strongly correlated in the two algorithms, but there is little overlap in fast spindle density and slow spindle parameters in general. The agreement between fixed and manually determined sleep spindle frequencies is limited, especially in case of slow spindles. This is the most likely reason for the poor agreement between the two detection methods in case of slow spindle parameters. Our results suggest that while various algorithms may reliably detect fast spindles, a more sophisticated algorithm primed to individual spindle frequencies is necessary for the detection of slow spindles as well as individual variations in the number of spindles in general. (shrink)