Delay differential analysis for dynamical sleep spindle detection.
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
15 03 2019
15 03 2019
Historique:
received:
16
05
2018
revised:
04
01
2019
accepted:
20
01
2019
pubmed:
2
2
2019
medline:
29
7
2020
entrez:
2
2
2019
Statut:
ppublish
Résumé
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 F This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.
Sections du résumé
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
CONCLUSIONS
This additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.
Identifiants
pubmed: 30707917
pii: S0165-0270(19)30020-2
doi: 10.1016/j.jneumeth.2019.01.009
pmc: PMC6447286
mid: NIHMS1521353
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12-21Subventions
Organisme : NIBIB NIH HHS
ID : R01 EB009282
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS104368
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH020002
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB026899
Pays : United States
Organisme : NINDS NIH HHS
ID : F99 NS105204
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.
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