Delay differential analysis for dynamical sleep spindle detection

• Published in: Journal of neuroscience methods Vol. 316; pp. 12 - 21
• Main Authors: Sampson, Aaron L., Lainscsek, Claudia, Gonzalez, Christopher E., Ulbert, István, Devinsky, Orrin, Fabó, Dániel, Madsen, Joseph R., Halgren, Eric, Cash, Sydney S., Sejnowski, Terrence J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.03.2019
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2019.01.009

•Delay differential analysis (DDA) is a powerful non-linear tool for EEG data analysis.•DDA features can be used to detect sleep spindles quickly and reliably.•DDA provides a novel and unique time-domain measure of spindle activity.•DDA is the best and one of the fastest of the tested sleep spindle detectors. 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. 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. We show that this approach is computationally fast, generalizable, requires minimal preprocessing, and provides excellent agreement with human scoring. 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 F1 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 F1 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. This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.