Dynamic models for nonstationary signal segmentation

This paper investigates Hidden Markov Models (HMMs) in which the observatio ns are generated from an autoregressive (AR) model. The overall model perfo rms nonstationary spectral analysis and automatically segments a time serie s into discrete dynamic regimes. Because learning in HMMs is sensitive to i nitial conditions, we initialize the HMM model with parameters derived from a cluster analysis of Kalman filter coefficients. An important aspect of t he Kalman filter implementation is that the state noise is estimated on-lin e. This allows for an initial estimation of AR parameters for each of the d ifferent 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. (C) 1999 Academic Press.