PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care
Christopher Williams and John Quinn
In: Neural Information Processing Systems, 5-8 December, 2005, Vancouver, Canada.

Abstract

The observed physiological dynamics of an infant receiving intensive care are affected by many possible factors, including interventions to the baby, the operation of the monitoring equipment and the state of health. The Factorial Switching Kalman Filter can be used to infer the presence of such factors from a sequence of observations, and to estimate the true values where these observations have been corrupted. We apply this model to clinical time series data and show it to be effective in identifying a number of artifactual and physiological patterns.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2408
Deposited By:Christopher Williams
Deposited On:22 November 2006