Factorial switching linear dynamical systems for physiological condition monitoring
Seminar Room 1, Newton Institute
Condition monitoring often involves the analysis of measurements taken from a system which "switches" between different modes of operation in some way. Given a sequence of observations, the task is to infer which possible condition (or "switch setting") of the system is most likely at each time frame. In this paper we describe the use of factorial switching linear dynamical models for such problems. A particular advantage of this construction is that it provides a framework in which domain knowledge about the system being analysed can easily be incorporated.
We demonstrate the flexibility of this type of model by applying it to the problem of monitoring the condition of a premature baby receiving intensive care. The state of health of a baby cannot be observed directly, but different underlying factors are associated with particular patterns of measurements, e.g. in the heart rate, blood pressure and temperature. We use the model to infer the presence of two different types of factors: common, recognisable regimes (e.g. certain artifacts or common physiological phenomena), and novel patterns which are clinically significant but have unknown cause. Experimental results are given which show the developed methods to be effective on real intensive care unit monitoring data.
Joint work with John Quinn and Neil McIntosh
- http://www.dai.ed.ac.uk/homes/ckiw/neonatal/index.html - Web page describing Edinburgh work on Condition Monitoring in Premature Babies
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