Automated sleep classification with chronic neural implants in freely behaving canines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00079689" target="_blank" >RIV/00159816:_____/23:00079689 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21460/23:00367688 RIV/68407700:21730/23:00367688 RIV/00216305:26220/23:PU148846
Result on the web
<a href="https://iopscience.iop.org/article/10.1088/1741-2552/aced21" target="_blank" >https://iopscience.iop.org/article/10.1088/1741-2552/aced21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1741-2552/aced21" target="_blank" >10.1088/1741-2552/aced21</a>
Alternative languages
Result language
angličtina
Original language name
Automated sleep classification with chronic neural implants in freely behaving canines
Original language description
Objective. Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function. Approach. Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines. Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 & PLUSMN; 0.055 and a Cohen's Kappa score of 0.786 & PLUSMN; 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 & PLUSMN; 2.34 cycles per day vs. 22.39 & PLUSMN; 3.88 cycles per night; p < 0.001), shorter NREM cycle durations (13.83 & PLUSMN; 8.50 min per day vs. 15.09 & PLUSMN; 8.55 min per night; p < 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 & PLUSMN; 0.09, REM 0.12 & PLUSMN; 0.09 per day vs. NREM 0.80 & PLUSMN; 0.08, REM 0.20 & PLUSMN; 0.08 per night; p < 0.001). Significance. These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20601 - Medical engineering
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Neural Engineering
ISSN
1741-2560
e-ISSN
1741-2552
Volume of the periodical
20
Issue of the periodical within the volume
4
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
046025
UT code for WoS article
001045228900001
EID of the result in the Scopus database
—