Automated sleep classification with chronic neural implants in freely behaving canines
Identifikátory výsledku
Kód výsledku v 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>
Nalezeny alternativní kódy
RIV/68407700:21460/23:00367688 RIV/68407700:21730/23:00367688 RIV/00216305:26220/23:PU148846
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Automated sleep classification with chronic neural implants in freely behaving canines
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Automated sleep classification with chronic neural implants in freely behaving canines
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20601 - Medical engineering
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Neural Engineering
ISSN
1741-2560
e-ISSN
1741-2552
Svazek periodika
20
Číslo periodika v rámci svazku
4
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
10
Strana od-do
046025
Kód UT WoS článku
001045228900001
EID výsledku v databázi Scopus
—