PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis
Iead Rezek and Stephen J. Roberts
In: Probabilistic Modelling in Bioinformatics and Meical Informatics (2005) Springer Verlag , London, U.K. , pp. 419-450. ISBN 1-85233-788-8

Abstract

Hidden Markov Models (HMM) have proven to be very useful in a variety of biomedical applications. The most established method for estimating HMM parameters is the maximum likelihood method which has shortcomings, such as repeated estimation and penalisation of the likelihood score, that are well known. This paper describes an Variational learning approach to try and improve on the maximum-likelihood estimators. Emphasis lies on the fact, that for HMMs with observation models that are from the exponential family of distributions, all HMM parameters and hidden state variables can be derived from a single loss function, namely the Kullback-Leibler Divergence. Practical issues, such as model initialisation and choice of model order, are described. The paper concludes by application of 3 types of observation model HMMs to a variety of biomedical data, such as EEG and ECG, from different physiological experiments and conditions.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Brain Computer Interfaces
ID Code:1124
Deposited By:Iead Rezek
Deposited On:13 October 2005