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

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

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 journal › Article › peer-review

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 language	English
Pages (from-to)	246-260
Number of pages	15
Journal	Theory in Biosciences
Volume	118
Issue number	3-4
Publication status	Published - 1999 Dec

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

Cite this

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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.",

keywords = "Dynamical mode detection, EEG, Hidden Markov models, Nonstationarity, Segmentation, Sleep, Time series",

author = "Stefan Liehr and Klaus Pawelzik and Jens Kohlmorgen and M{\"u}ller, {Klaus Robert}",

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AU - Liehr, Stefan

AU - Pawelzik, Klaus

AU - Kohlmorgen, Jens

AU - Müller, Klaus Robert

PY - 1999/12

Y1 - 1999/12

N2 - 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.

AB - 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.

KW - Dynamical mode detection

KW - EEG

KW - Hidden Markov models

KW - Nonstationarity

KW - Segmentation

KW - Sleep

KW - Time series

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M3 - Article

AN - SCOPUS:0033375190

SN - 1431-7613

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EP - 260

JO - Theory in Biosciences

JF - Theory in Biosciences

IS - 3-4

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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