Delay differential analysis for dynamical sleep spindle detection

Abstract Background Sleep spindles are involved in memory consolidation and other cognitive functions. Numerous automated methods for detection of spindles have been proposed; most of these rely on spectral analysis in some form. However, none of these approaches are ideal, and novel approaches to the problem could provide additional insights. New method Here, we apply delay differential analysis (DDA), a time-domain technique based on nonlinear dynamics to detect sleep spindles in human intracranial sleep data, including laminar electrode, stereoelectroencephalogram (sEEG), and electrocorticogram (ECoG) recordings. Results We show that this approach is computationally fast, generalizable, requires minimal preprocessing, and provides excellent agreement with human scoring. Comparison with existing methods We compared the method with established methods on a set of intracranial recordings and this method provided the highest agreement with human expert scoring when evaluated with F 1 score while being the second-fastest to run. We also compared the results on the DREAMS surface EEG data, where the method produced a higher average F 1 score than all other tested methods except the automated detections published with the DREAMS data. Further, in addition to being a fast and reliable method for spindle detection, DDA also provides a novel characterization of spindle activity based on nonlinear dynamical content of the data. Conclusions This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.