title: Dynamic models for nonstationary signal segmentation.
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.
authors.affiliation: Neural Systems Research Group, Department of Electrical and Electronic Engineering, Imperial College of Science, Technology and Medicine, SW7 2BT, London, UK.
authors.forename: W D
authors.initials: WD
authors.lastname: Penny
authors.forename: S J
authors.initials: SJ
authors.lastname: Roberts
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D006225:Hand; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009068:Movement / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology