Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms

Roland Langrock, Bruce J. Swihart, Brian S. Caffo, Naresh M. Punjabi, Ciprian M. Crainiceanu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


In this manuscript, we consider methods for the analysis of populations of electroencephalogram signals during sleep for the study of sleep disorders using hidden Markov models (HMMs). Notably, we propose an easily implemented method for simultaneously modeling multiple time series that involve large amounts of data. We apply these methods to study sleep-disordered breathing (SDB) in the Sleep Heart Health Study (SHHS), a landmark study of SDB and cardiovascular consequences. We use the entire, longitudinally collected, SHHS cohort to develop HMM population parameters, which we then apply to obtain subject-specific Markovian predictions. From these predictions, we create several indices of interest, such as transition frequencies between latent states. Our HMM analysis of electroencephalogram signals uncovers interesting findings regarding differences in brain activity during sleep between those with and without SDB. These findings include stability of the percent time spent in HMM latent states across matched diseased and non-diseased groups and differences in the rate of transitioning.

Original languageEnglish (US)
Pages (from-to)3342-3356
Number of pages15
JournalStatistics in Medicine
Issue number19
StatePublished - Aug 30 2013


  • Dirichlet distribution
  • Fourier power spectrum
  • Independent mixture
  • Markov chain
  • Sleep-disordered breathing

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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