Automated sleep classification with chronic neural implants in freely behaving canines.

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, accelerometery, scalp EEG and iEEG monitoring. The video, scalp EEG, and accelerometery 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 (NREM 1, 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 ± 0.055 and a Cohen's Kappa score of 0.786 ± 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 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night; p<0.001), shorter NREM cycle durations (13.83 ± 8.50 mins per day vs. 15.09 ± 8.55 mins 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 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 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.