PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automating the Calibration of a Neonatal Condition Monitoring System
Christopher Williams and Ioan Stanculescu
Lecture Notes in Computer Science Volume 6747, pp. 240-249, 2011. ISSN 0302-9743

Abstract

Condition monitoring of premature babies in intensive care can be carried out using a Factorial Switching Linear Dynamical System (FSLDS) [15]. A crucial part of training the FSLDS is the manual calibration stage, where an interval of normality must be identified for each baby that is monitored. In this paper we replace this manual step by using a classifier to predict whether an interval is normal or not. We show that the monitoring results obtained using automated calibration are almost as good as those using manual calibration.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8775
Deposited By:Ioan Stanculescu
Deposited On:21 February 2012