Publication detail

Automated sleep classification with chronic neural implants in freely behaving canines

MIVALT, F. SLADKY, V. WORRELL, S. GREGG, N.M. BALZEKAS, I. KIM, I. CHANG, S.Y. MONTONYE, D.R. DUQUE-LOPEZ, A. KRAKOROVA, M. PRIDALOVA, T. LEPKOVA, K. BRINKMANN, B.H. MILLER, K.J. VAN GOMPEL, J.J. DENISON, T. KAUFMANN, T.J. MESSINA, S.A. St LOUIS, E.K. KREMEN, V. WORRELL, G.A.

Original Title

Automated sleep classification with chronic neural implants in freely behaving canines

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

sleep classification; implantable devices for sensing and stimulation; intracranial EEG; canine

Authors

MIVALT, F.; SLADKY, V.; WORRELL, S.; GREGG, N.M.; BALZEKAS, I.; KIM, I.; CHANG, S.Y.; MONTONYE, D.R.; DUQUE-LOPEZ, A.; KRAKOROVA, M.; PRIDALOVA, T.; LEPKOVA, K.; BRINKMANN, B.H.; MILLER, K.J.; VAN GOMPEL, J.J.; DENISON, T.; KAUFMANN, T.J.; MESSINA, S.A.; St LOUIS, E.K.; KREMEN, V.; WORRELL, G.A.

Released

10. 8. 2023

Publisher

IOP Publishing Ltd

Location

BRISTOL

ISBN

1741-2560

Periodical

J NEURAL ENG

Year of study

20

Number

4

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

10

Pages count

10

URL

https://iopscience.iop.org/article/10.1088/1741-2552/aced21

BibTex

@article{BUT184430,
  author="MIVALT, F. and SLADKY, V. and WORRELL, S. and GREGG, N.M. and BALZEKAS, I. and KIM, I. and CHANG, S.Y. and MONTONYE, D.R. and DUQUE-LOPEZ, A. and KRAKOROVA, M. and PRIDALOVA, T. and LEPKOVA, K. and BRINKMANN, B.H. and MILLER, K.J. and VAN GOMPEL, J.J. and DENISON, T. and KAUFMANN, T.J. and MESSINA, S.A. and St LOUIS, E.K. and KREMEN, V. and WORRELL, G.A.",
  title="Automated sleep classification with chronic neural implants in freely behaving canines",
  journal="J NEURAL ENG",
  year="2023",
  volume="20",
  number="4",
  pages="1--10",
  doi="10.1088/1741-2552/aced21",
  issn="1741-2560",
  url="https://iopscience.iop.org/article/10.1088/1741-2552/aced21"
}