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Dynamic models for nonstationary signal segmentation.

W D Penny·S J Roberts
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Abstract

This paper investigates Hidden Markov Models (HMMs) in which the observations are generated from an autoregressive (AR) model. The overall model performs nonstationary spectral analysis and automatically segments a time series into discrete dynamic regimes. Because learning in HMMs is sensitive to initial conditions, we initialize the HMM model with parameters derived from a cluster analysis of Kalman filter coefficients. An important aspect of the Kalman filter implementation is that the state noise is estimated on-line. This allows for an initial estimation of AR parameters for each of the different dynamic regimes. These estimates are then fine-tuned with the HMM model. The method is demonstrated on a number of synthetic problems and on electroencephalogram data.

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AlgorithmsElectroencephalographyHandHumansMarkov ChainsModels, StatisticalMovementSignal Processing, Computer-AssistedSleepJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

W D Penny, S J Roberts. 1999. Dynamic models for nonstationary signal segmentation.. https://doi.org/10.1006/cbmr.1999.1511

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