Neonatal Baby Monitoring

In this thesis we investigate the use of probabilistic graphical models for neonatal baby monitoring applications. In particular, we concentrate on detecting artefact patterns in physiological data using a conditional Gaussian approach. We describe a system that learns the necessary parameters from the given data and produces marginal posterior probabilities for the latent variables that have been used to model the artefact processes. It should be emphasised that the current system does not include the temporal evolution of the measured signals, but we indicate how this can be done within the presented framework. We also discuss our approach in the context of prior work and present ways to overcome identified problems.