Condition Monitoring in Premature Babies
The project is concerned with
detecting patterns in the monitoring traces of
premature babies in intensive care, using probabilistic modelling. The
goal is to identify different types of artifact and pathology in real
time based on characteristic patterns in the data. Currently there
are a high number of false alarms in neonatal units due to events like a
baby being handled, or a monitoring probe becoming dislodged.
By identifying such artifactal factors automatically the aim is to
reduce false alarms, and thus to focus attention on the state of
health of the baby.
The project is a collaboration between Prof Neil McIntosh
(Child Life and Health,
University of Edinburgh), Dr Yvonne Freer (Simpson Centre
for Reproductive Health, The Royal Infirmary of Edinburgh),
and Prof Chris Williams
(School of Informatics,
University of Edinburgh). The initial work was carried out by
John Quinn, and
was supported by a grant from the premature
baby charity BLISS. The research
is being further developed by
- A Hierarchical Switching Linear Dynamical System Applied to the
Detection of Sepsis in Neonatal Condition Monitoring
- Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer
Proceedings of the 30th Conference on Uncertainty in
Artificial Intelligence (UAI 2014).
- Autoregressive Hidden Markov Models for the Early Detection of
- Ioan Stanculescu, Christopher K.I. Williams, and Yvonne Freer
Accepted for publication in
IEEE Journal of Biomedical and Health Informatics, 1 Dec 2013.
Published in J-BHI 18(5) 1560-1570, September 2014.
- Automating the Calibration of a Neonatal Condition Monitoring System
- C.K.I. Williams and I. Stanculescu.
In Proc AIME
eds M. Peleg, N. Lavrač, and C. Combi, LNAI 6747, pp. 240--249. Springer (2011)
- Physiological Monitoring with
Factorial Switching Linear Dynamical Systems
- J.A. Quinn and C.K.I. Williams.
Chapter appearing in Bayesian Time Series Models, eds. D. Barber,
T. Cemgil, S. Chiappa, Cambridge University Press, 2011.
- Factorial Switching Linear Dynamical Systems applied to
Physiological Condition Monitoring
- John A. Quinn, Christopher K.I. Williams, Neil McIntosh.
Accepted to IEEE Trans. on Pattern Analysis and Machine Intelligence
published T-PAMI 31(9) pp 1537-1551 (2009). Matlab code is available.
- Known Unknowns: Novelty Detection in Condition Monitoring
- John A. Quinn, Christopher K. I. Williams.
Invited paper in Proc 3rd
Iberian Conference on Pattern Recognition and Image Analysis
eds J. Marti, J. M. Benedi, A. M. Mendonca,
J. Serrat, LNCS 4477
pp 1-6, Springer-Verlag (2007).
- Factorial Switching Kalman Filters for Condition Monitoring in
Neonatal Intensive Care
- Christopher K. I. Williams, John Quinn, Neil McIntosh
In Advances in Neural Information Processing Systems
18, eds. Y. Weiss, B. Schoelkopf, J. C. Platt, MIT Press (2006)