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J Neurosci Methods. 2019 Mar 15;316:12-21. doi: 10.1016/j.jneumeth.2019.01.009. Epub 2019 Jan 30.

Delay differential analysis for dynamical sleep spindle detection.

Author information

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.

PMID:
30707917
PMCID:
PMC6447286
[Available on 2020-03-15]
DOI:
10.1016/j.jneumeth.2019.01.009

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