Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements

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15 Scopus citations


The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) (p < 0.01; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman's rho 0.43-0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

Original languageEnglish
Article number1650017
JournalInternational Journal of Neural Systems
Issue number4
StatePublished - Jun 1 2016

Bibliographical note

Funding Information:
This research was supported by the National Institute of Neurological Disorders and Stroke Grants NS083218 and NS065451. The authors thank Chris Schildt, Asmaa Ajwad, and Ting Zhang for their assistance with animal procedures.

Publisher Copyright:
© 2016 World Scientific Publishing Company.


  • Automatic sleep scoring
  • EEG
  • behavior
  • bout duration
  • hidden Markov model
  • mouse sleep
  • sleep dynamics
  • unsupervised
  • vigilance state

ASJC Scopus subject areas

  • Computer Networks and Communications


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