Dynamic models for nonstationary signal segmentation

William D Penny, Stephen J. Roberts

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)483-502
Number of pages20
JournalComputers and Biomedical Research
Issue number6
Publication statusPublished - Dec 1999


  • Algorithms
  • Electroencephalography
  • Hand
  • Humans
  • Markov Chains
  • Statistical Models
  • Movement
  • Computer-Assisted Signal Processing
  • Sleep

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