Advanced sleep spindle identification with neural networks

  • Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.

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Metadaten
Author:Lars Kaulen, Justus T. C. Schwabedal, Jules Schneider, Philipp Ritter, Stephan BialonskiORCiD
DOI:https://doi.org/10.1038/s41598-022-11210-y
DOI:https://doi.org/10.21269/9997
ISSN:2045-2322
Parent Title (English):Scientific Reports
Publisher:Springer Nature
Place of publication:London
Document Type:Article
Language:English
Year of Completion:2022
Publishing Institution:Fachhochschule Aachen
Date of the Publication (Server):2022/05/11
Volume:12
Issue:Article number: 7686
First Page:1
Last Page:10
Note:
Corresponding author: Stephan Bialonski
Link:https://doi.org/10.1038/s41598-022-11210-y
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
open_access (DINI-Set):open_access
collections:Verlag / Springer Nature
Open Access / Gold
Geförderte OA-Publikationen / DEAL Springer
Licence (German): Creative Commons - Namensnennung