Hidden Markov mixtures of experts with an application to EEG recordings from sleep

Stefan Liehr, Klaus Pawelzik, Jens Kohlmorgen, Klaus Robert Müller

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

We present a framework for the analysis of time series from nonstationary dynamical systems that operate in multiple modes. The method detects mode changes and identifies the underlying subdynamics. It unifies the mixtures of experts approach and a generalized hidden Markov model with an input-dependent transition matrix. The adaptation of the individual experts and of the hidden Markov model is performed simultaneously. We illustrate the capabilities of our algorithm for chaotic time series and EEG recordings from human subjects during afternoon naps.

Original languageEnglish
Pages (from-to)246-260
Number of pages15
JournalTheory in Biosciences
Volume118
Issue number3-4
Publication statusPublished - 1999 Dec
Externally publishedYes

Keywords

  • Dynamical mode detection
  • EEG
  • Hidden Markov models
  • Nonstationarity
  • Segmentation
  • Sleep
  • Time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Ecology, Evolution, Behavior and Systematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Hidden Markov mixtures of experts with an application to EEG recordings from sleep'. Together they form a unique fingerprint.

  • Cite this

    Liehr, S., Pawelzik, K., Kohlmorgen, J., & Müller, K. R. (1999). Hidden Markov mixtures of experts with an application to EEG recordings from sleep. Theory in Biosciences, 118(3-4), 246-260.