Aaron L Sampson1, Claudia Lainscsek2, Christopher E Gonzalez3, István Ulbert4, Orrin Devinsky5, Dániel Fabó6, Joseph R Madsen7, Eric Halgren8, Sydney S Cash9, Terrence J Sejnowski10. 1. Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: asampson@ucsd.edu. 2. Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA. 3. Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA. 4. Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, H-1083 Budapest, Hungary. 5. New York University Comprehensive Epilepsy Center, New York, NY 10016, USA. 6. Epilepsy Centrum, National Institute of Clinical Neurosciences, Budapest, Hungary. 7. Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA. 8. Departments of Radiology and Neurosciences, University of California San Diego, La Jolla, CA 92093, USA. 9. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, MA 02114, USA. 10. Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA; Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
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 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. CONCLUSIONS: This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.
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 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. CONCLUSIONS: This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.
Authors: Fabio Ferrarelli; Reto Huber; Michael J Peterson; Marcello Massimini; Michael Murphy; Brady A Riedner; Adam Watson; Pietro Bria; Giulio Tononi Journal: Am J Psychiatry Date: 2007-03 Impact factor: 18.112
Authors: P Y Ktonas; S Golemati; P Xanthopoulos; V Sakkalis; M D Ortigueira; H Tsekou; M Zervakis; T Paparrigopoulos; C R Soldatos Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2007
Authors: Manuel Schabus; Georg Gruber; Silvia Parapatics; Cornelia Sauter; Gerhard Klösch; Peter Anderer; Wolfgang Klimesch; Bernd Saletu; Josef Zeitlhofer Journal: Sleep Date: 2004-12-15 Impact factor: 5.849
Authors: Claudia Lainscsek; Aaron L Sampson; Robert Kim; Michael L Thomas; Karen Man; Xenia Lainscsek; Neal R Swerdlow; David L Braff; Terrence J Sejnowski; Gregory A Light Journal: Proc Natl Acad Sci U S A Date: 2019-02-11 Impact factor: 11.205