Condition Monitoring in the Adult ICU
The presence of artifact in intensive care monitoring data is a major
problem; e.g. maintaining blood pressure in critically ill patients
is a key management goal and yet it is the physiological variable most
prone to error. In addition to real-time monitoring, artifact
detection is necessary for the proper audit or trial of therapies.
This project has developed and validated the Factorial Switching
Linear Dynamical System (FSLDS) and the Discriminative Switching
Linear Dynamical System (DSLDS) for the detection, removal and
cleaning of artifact from vital signs data.
- Input-Output Non-Linear Dynamical Systems applied to
Physiological Condition Monitoring
- Konstantinos Georgatzis, Christopher K.I. Williams and
Christopher Hawthorne. Proc
Machine Learning in Health Care,
JMLR W&C Track Volume 56, 2016.
- Detecting Artifactual Events in Vital Signs Monitoring Data.
- Partha Lal, Christopher K. I. Williams, Konstantinos Georgatzis,
Christopher Hawthorne, Paul McMonagle, Ian Piper, Martin Shaw.
Technical report, October 2015.
A slightly revised version is published as a chapter in
Machine Learning for Healthcare Technologies,
ed. David A. Clifton, Institution of Engineering and Technology, 2016.
- Discriminative Switching Linear Dynamical Systems applied to
- Konstantinos Georgatzis, Christopher K. I. Williams. In
Proc UAI 2015.
An earlier version was posted on arXiv 24 April 2015
- Automatic Detection of Artifact and Events in Vital-Signs
Traces: A Comparison of Two Machine Learning-Based Models.
- Abstract presented at the British Neurosurgical Research Group Meeting
in March 2015.
- Artifact in physiological data collected from brain injured
patients: quantifying the problem and providing a solution through a
factorial switching linear dynamical systems approach.
- K. Georgatzis, P. Lal, C. Hawthorne, M. Shaw, I. Piper,
C. Tarbert, R. Donald, C. K. I. Williams.
This initial work on the project was presented orally at the 15th International
Symposium on Intracranical Pressure and Brain Monitoring on 6 November
2013; Published in Intracranial Pressure and Brain Monitoring XV,
Volume 122 of the series Acta Neurochirurgica Supplement pp 301-305,
The project was funded by grant number CHZ/4/801 from the
Chief Scientist Office (Scotland): Improving Decision Support for
Treating Arterial Hypotension in Adult Patients During their
Management in Intensive Care, May 2013-Apr 2015. PI: Prof Chris Williams,
coIs: Dr Ian Piper, Prof John Kinsella, Prof Peter Andrews, Mr
The work of PhD student Konstantinos Georgatzis was supported by