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.
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.