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Penny, William D 
ORCID: https://orcid.org/0000-0001-9064-1191 and Roberts, Stephen J.
(1999)
Dynamic models for nonstationary signal segmentation.
Computers and Biomedical Research, 32 (6).
pp. 483-502.
ISSN 0010-4809
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: | 20 Apr 2023 00:33 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/64644 |
| DOI: | 10.1006/cbmr.1999.1511 |
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