Background: Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. New method: Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. Results: Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. Comparison with existing methods: Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. Conclusions: This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.
|Number of pages||11|
|Journal||Journal of Neuroscience Methods|
|State||Published - Feb 1 2016|
Bibliographical noteFunding Information:
This research was supported in part by the National Institute of Neurological Disorders and Stroke (grants NS083218 and NS065451 ) and by the Kentucky Spinal Cord and Head Injury Research Trust ( KSCHIRT ; grant 10-5A ). The authors thank Chris Schildt, B.S., Asmaa Ajwad, M.Sc., and Ting Zhang, M.S., for their assistance with animal procedures.
© 2015 Elsevier B.V.
- Hidden Markov model
- High-throughput screening
- Noninvasive sleep scoring
ASJC Scopus subject areas
- Neuroscience (all)