Gutenberg Open Science: Automated real-time EEG sleep spindle detection for brain-state-dependent brain stimulation

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DC Element	Wert	Sprache
dc.contributor.author	Hassan, Umair	-
dc.contributor.author	Feld, Gordon B.	-
dc.contributor.author	Bergmann, Til Ole	-
dc.date.accessioned	2023-01-09T11:22:35Z	-
dc.date.available	2023-01-09T11:22:35Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8553	-
dc.description.abstract	Sleep spindles are a hallmark electroencephalographic feature of non-rapid eye movement sleep, and are believed to be instrumental for sleep-dependent memory reactivation and consolidation. However, direct proof of their causal relevance is hard to obtain, and our understanding of their immediate neurophysiological consequences is limited. To investigate their causal role, spindles need to be targeted in real-time with sensory or non-invasive brain-stimulation techniques. While fully automated offline detection algorithms are well established, spindle detection in real-time is highly challenging due to their spontaneous and transient nature. Here, we present the real-time spindle detector, a robust multi-channel electroencephalographic signal-processing algorithm that enables the automated triggering of stimulation during sleep spindles in a phase-specific manner. We validated the real-time spindle detection method by streaming pre-recorded sleep electroencephalographic datasets to a real-time computer system running a Simulink® Real-Time™ implementation of the algorithm. Sleep spindles were detected with high levels of Sensitivity (~83%), Precision (~78%) and a convincing F1-Score (~81%) in reference to state-of-the-art offline algorithms (which reached similar or lower levels when compared with each other), for both naps and full nights, and largely independent of sleep scoring information. Detected spindles were comparable in frequency, duration, amplitude and symmetry, and showed the typical time–frequency characteristics as well as a centroparietal topography. Spindles were detected close to their centre and reliably at the predefined target phase. The real-time spindle detection algorithm therefore empowers researchers to target spindles during human sleep, and apply the stimulation method and experimental paradigm of their choice.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY-NC-ND	*
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Automated real-time EEG sleep spindle detection for brain-state-dependent brain stimulation	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8537	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Journal of sleep research	de
jgu.journal.volume	31	de
jgu.journal.issue	6	de
jgu.pages.alternative	e13733	de
jgu.publisher.year	2022	-
jgu.publisher.name	Wiley-Blackwell	de
jgu.publisher.place	Oxford u.a.	de
jgu.publisher.issn	1365-2869	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1111/jsr.13733	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Lebenswissenschaften	de
Enthalten in den Sammlungen:	DFG-491381577-H

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