Dynamic models for nonstationary signal segmentation

Penny, William D and Roberts, Stephen J. (1999) Dynamic models for nonstationary signal segmentation. Computers and Biomedical Research, 32 (6). pp. 483-502. ISSN 0010-4809

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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.

Item Type:	Article
Additional Information:	Copyright 1999 Academic Press.
Uncontrolled Keywords:	algorithms,electroencephalography,hand,humans,markov chains,statistical models,movement,computer-assisted signal processing,sleep
Faculty \ School:	Faculty of Social Sciences > School of Psychology
UEA Research Groups:	Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences
Depositing User:	Pure Connector
Date Deposited:	23 Aug 2017 05:04
Last Modified:	06 Feb 2025 07:23
URI:	https://ueaeprints.uea.ac.uk/id/eprint/64644
DOI:	10.1006/cbmr.1999.1511

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