Systematic performance evaluation of a continuous-scale sleep depth measure.

In this article, systematic performance evaluation of a continuous-scale sleep depth measure will be discussed. Our main objective has been to select the adjustable analysis parameters such that the best possible correspondence between method output and standard visual sleep staging could be achieved. Sleep depth estimation was based on continuous monitoring of short-time EEG synchronization through the local mean frequency of the EEG. During the experiments, total amount of 752 different combinations of four adjustable parameters were compared based on all-night sleep EEG recordings of 15 healthy subjects. Optimization strategy applied was based on maximizing the weighted average of pair-wise separabilities of EEG mean frequency distributions in all the standard sleep stage pairs. Finally, robustness of the optimized parameters was verified with an independent dataset of 34 all-night sleep recordings. Our results show that clear topological differences between brain hemispheres and different electrode locations exist. Performance improvements of even 20-30% units can be achieved by proper selection of analysis parameters and the EEG derivation used for the analysis. Remarkable independence of system performance on the analysis window length leads to improved temporal resolution compared to that achieved through standard visual analysis. In addition to giving practical suggestions on the parameter selection, we also propose a possible method for improving stage separability especially between S2 and REM.