Computational neuroscienceNoninvasive dissection of mouse sleep using a piezoelectric motion sensor

- A piezoelectric sensor can accurately differentiate sleep from wake and sense breathing in mice.
- Piezoelectric signal features were clustered into multiple states using a hidden Markov model.
- Sleep states that differed in breathing regularity were strongly correlated with REM/NREM.
- This technology will permit high-throughput screening of sleep traits for genetic or drug studies.

BackgroundChanges 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 methodVigilance 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.ResultsUsing 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 methodsUnlike 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.ConclusionsThis approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.