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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.
|Title of host publication||Advances in Neural Information Processing Systems 18|
|Number of pages||8|
|Publication status||Published - 2006|
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- 1 Finished
1/12/03 → 30/09/08